Performance of UAV Radar Concepts for Naval Defencecradpdf.drdc-rddc.gc.ca/PDFS/unc94/p531349_a1b.pdf · · 2012-08-03Sommaire Performance of UAV Radar Concepts for Naval Defence

Performance of UAV Radar Concepts

for Naval Defence 11aw12 Final Report

Peter W. Moo

TECHNICAL REPORT

DRDC Ottawa TR 2008-340

March 2009

Performance of UAV Radar Concepts for Naval

Defence11aw12 Final Report

Peter W. Moo

Defence R&D Canada – Ottawa


Technical Report


March 2009

Principal Author

Original signed by Peter W. Moo

Peter W. Moo

Approved by

Original signed by D. Dyck

D. Dyck

Head/RS

Approved for release by

Original signed by P. Lavoie

P. Lavoie

Head/Document Review Panel

c© Her Majesty the Queen in Right of Canada as represented by the Minister of National

Defence, 2009

c© Sa Majeste la Reine (en droit du Canada), telle que representee par le ministre de la

Defense nationale, 2009

Abstract

This report analyzes the performance of a bistatic uninhabited aerial vehicle (UAV) radar

to detect cruise missile threats beyond a ship’s horizon. The bistatic system, together with

adaptive signal processing is shown to provide self-defence capabilities against targets that

are heading towards the ship. For area air defence, the UAV radar may not be able to detect

some targets, depending on the bistatic geometry. A monostatic UAV radar for small boat

defence is also developed. The radar cross section (RCS) of a small boat is modelled,

and it is shown that the median monostatic RCS is 1 m2. Performance of the UAV radar

is analyzed by simulating the small boat scenario in RLSTAP. With the use of Factored

space-time adaptive processing (STAP) to reduce clutter power, the resulting minimum

detectable velocity is shown to be between 2.8 m/s and 3.8 m/s. Payload sizes and weights

are estimated for the proposed radars.

Resume

Le present rapport analyse les performances d’un radar bistatique d’UAV utilise pour

detecter des menaces sous forme de missiles de croisiere au-dela de l’horizon d’un na-

vire. On montre que le systeme bistatique, combine au traitement de signaux adaptatif,

offre des capacites d’autodefense contre des cibles qui s’approchent du navire. Aux fins de

la defense aerienne sectorielle, il se peut que le radar d’UAV ne soit pas capable de detecter

certaines cibles, tout dependant de la geometrie bistatique. Un radar monostatique d’UAV

servant a la defense contre les petits bateaux est egalement en cours de developpement.

La surface equivalente radar (SER) d’un petit bateau est modelise, et l’on montre que la

SER monostatique moyenne est de 1 m2. Les performances du radar d’UAV sont analysees

par simulation du scenario d’un petit bateau dans RLSTAP. Grace a l’utilisation du traite-

ment STAP pondere pour reduire la puissance du clutter, on montre que la vitesse minimale

detectable resultante est comprise entre 2,8 m/s et 3,8 m/s.

DRDC Ottawa TR 2008-340 i

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ii DRDC Ottawa TR 2008-340

Executive summary

Performance of UAV Radar Concepts for Naval Defence

Peter W. Moo; DRDC Ottawa TR 2008-340; Defence R&D Canada – Ottawa;

March 2009.

In naval operations, uninhabited aerial vehicles (UAV) have the potential to provide auxil-

iary sensors to ship-based sensors. The use of UAV radar is promising, but the design of

such a system and the resulting performance are unknown. In this report, the use of UAV

radar is considered for two distinct naval surveillance requirements: 1) the over-the horizon

detection of cruise missiles, and 2) the detection of small boat threats.

For the cruise missile requirement, the goal of the UAV radar is to detect all targets in

a barrier beyond the ship’s horizon. The shipborne radar can then be cued to detect the

target when it appears on the horizon. A cruise missile target with a radar cross section

(RCS) of 0.1 m2 and a velocity of Mach 0.75 was assumed. Against this threat, a previ-

ous analysis had proposed a bistatic radar with a receiver located above the ship and the

transmitter deployed in the direction of the expected threat. In this study, the performance

of this bistatic system was analyzed using simulation in RLSTAP. A high-pulse repetition

frequency (PRF) waveform was employed to allow for the unambiguous Doppler estimate

of the target. A ship self-defence configuration and two area air defence configurations

were considered. For ship self defence, the simulation analysis showed that the minimum

detectable velocity (MDV) was 46.3 m/s when space-time adaptive processing (STAP) was

employed. Furthermore, it was shown that the radar failed to detect crossing targets, which

are less of a threat in ship self-defence. For area air defence, the MDV was also 46.3 m/s

with STAP processing. However, targets heading for the ship-of-interest at an angle may

not be detected by the UAV radar, due to the larger bistatic angles associated with the radar

geometry. Therefore, the bistatic UAV radar is effective in providing ship self defence ca-

pability, but may be impeded in providing area air defence if the bistatic geometries are not

favourable. It is shown that the use of STAP processing enhances the improvement factor

and provides marginal decrease in MDV, compared to Doppler processing. It is estimated

that transmitter payload has a weight of 42 kg and would require a tactical UAV as a plat-

form. The receiver payload has a weight of 123 kg and would require a medium altitude

long endurance (MALE) UAV platform.

Against small boat threats, the goal of a UAV radar is to detect these targets at a range of 5

to 10 km from the ship. In this study, the monostatic and bistatic RCS of the Legend small

boat was analyzed using the software Rapport. It was shown that the median monostatic

RCS was 1 m2. A monostatic UAV radar was considered, and a parametric analysis of

this radar was carried out. The resulting radar was modelled in RLSTAP, using a low

PRF waveform. It was seen that Factored STAP had better performance against the small

boat threat, both in terms of higher improvement factor and lower MDV. For four simulated

DRDC Ottawa TR 2008-340 iii

scenarios, Factored STAP resulted in MDVs between 2.8 m/s and 3.8 m/s. The use of STAP

provides significant benefit in the detection of small boats by decreasing the MDV. Further

work in this area would involve verifying UAV radar performance using experimental data.

For small boat detection, the radar payload has an estimated weight of 67 kg and would

require a tactical UAV as a platform.

iv DRDC Ottawa TR 2008-340

Sommaire


Peter W. Moo ; DRDC Ottawa TR 2008-340 ; R & D pour la defense Canada –

Ottawa ; mars 2009.

En operations navales, les vehicules aeriens teleguides (UAV) ont le potentiel de fournir

des capteurs auxiliaires aux capteurs a bord des navires. L’utilisation du radar d’UAV est

prometteuse, mais la conception d’un tel systeme et les performances resultantes sont in-

connues. Dans le present rapport, l’utilisation du radar d’UAV est envisagee pour deux exi-

gences de surveillance navale distinctes : 1) detection transhorizon de missiles de croisiere ;

2) detection de menaces sous forme de petits bateaux.

Quant a l’exigence relative a la detection de missiles de croisiere, le radar d’UAV a pour

but de detecter toutes les cibles dans une barriere au-dela de l’horizon du navire. Le ra-

dar de navire peut ensuite tre prepare pour detecter la cible lorsqu’elle apparat a l’horizon.

On a presume comme cible un missile de croisiere d’une SER de 0,1 m2 et se deplaant

a une vitesse de Mach 0,75. Pour detecter une telle menace, une analyse anterieure avait

propose un radar bistatique avec une antenne de reception situee au-dessus du navire et

une antenne d’emission orientee dans la direction de la menace prevue. Dans la presente

etude, les performances de ce systeme bistatique sont analysees au moyen d’une simula-

tion dans RLSTAP. Une forme d’onde a haute FRI est utilisee pour permettre l’estimation

Doppler sans ambigute de la cible. On a etudie une configuration d’autodefense de navire

et deux configurations de defense aerienne. Aux fins de l’autodefense du navire, l’analyse

de simulation montre que la vitesse minimale detectable (MDV) est de 46,3 m/s lorsque

le traitement STAP est utilise. En outre, on montre que le radar n’a pas reussi a detecter

les cibles traversantes, qui sont une menace moindre pour l’autodefense des navires. En

defense aerienne sectorielle, la MDV est egalement de 46,3 m/s avec traitement STAP.

Toutefois, les cibles se rapprochant du navire d’intert a un certain angle peuvent ne pas

tre detectees par le radar d’UAV, a cause des angles bistatique plus grands associes a la

geometrie du radar. Par consequent, le radar bistatique d’UAV est efficace dans la mesure

o il augmente la capacite d’autodefense du navire, mais peut tre d’une efficacite limitee

en termes de defense aerienne sectorielle si la geometrie bistatique n’est pas favorable. On

montre que l’utilisation du traitement STAP augmente le facteur d’amelioration et entrane

une legere reduction de la MDV, comparativement au traitement Doppler.

Dans le cas des menaces sous forme de petits bateaux, le radar d’UAV a pour but de detecter

ces cibles a une distance de 5 a 10 km du navire. Dans la presente etude, les SER monosta-

tique et bistatique du petit bateau Legend sont analysees au moyen du logiciel Rapport. Il

est montre que la SER monostatique moyenne est de 1 m2. On a envisage un radar mono-

statique d’UAV et effectue une analyse parametrique de ce radar. Le radar a ete modelise

dans RLSTAP, au moyen d’une forme d’onde a basse FRI. On a constate que le traitement

DRDC Ottawa TR 2008-340 v

STAP pondere avait de meilleures performances vis-a-vis de la menace sous forme d’un

petit bateau, a la fois en augmentant le facteur d’amelioration et en reduisant la MDV. Pour

les quatre scenarios de simulation, le traitement STAP pondere a donne lieu a des MDV

comprises entre 2,8 m/s et 3,8 m/s. L’utilisation du traitement STAP donne un avantage

considerable dans la detection de petits bateaux grace a la reduction de la MDV. Les re-

cherches futures dans ce domaine consisteraient a verifier les performances du radar d’UAV

au moyen de donnees experimentales.

vi DRDC Ottawa TR 2008-340

Table of contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Resume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Sommaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Table of contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

List of tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Cruise Missile Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Bistatic Radar Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

4 Sea clutter modelling and prediction . . . . . . . . . . . . . . . . . . . . . . . . 9

4.1 Walker model for sea clutter Doppler spectrum . . . . . . . . . . . . . . . 9

4.2 Prediction of minimum detectable velocity for targets in sea clutter . . . . 10

5 Simulation and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5.1 RLSTAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5.2 Space-Time Adaptive Processing . . . . . . . . . . . . . . . . . . . . . . 19

6 Concept Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6.1 Ship Self-Defence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6.2 Area Air Defence, Configuration 1 . . . . . . . . . . . . . . . . . . . . . 31

6.3 Area Air Defence, Configuration 2 . . . . . . . . . . . . . . . . . . . . . 36

6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

7 Small Boat Radar Cross Section . . . . . . . . . . . . . . . . . . . . . . . . . . 42

8 Assessment of Small Boat Detection Performance . . . . . . . . . . . . . . . . . 45

DRDC Ottawa TR 2008-340 vii

9 Simulation of UAV Detection of Small Boats . . . . . . . . . . . . . . . . . . . 50

10 Payload Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

10.1 Bistatic radar for cruise missile detection . . . . . . . . . . . . . . . . . . 54

10.2 Monostatic radar for small boat detection . . . . . . . . . . . . . . . . . . 55

11 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Annex A: Bistatic Small Boat RCS . . . . . . . . . . . . . . . . . . . . . . . . . . 59

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

viii DRDC Ottawa TR 2008-340

List of figures

Figure 1: Illustration of monostatic and bistatic UAV radar concepts. . . . . . . . . 4

Figure 2: Minimum receiver antenna diameter as a function of receiver ground

range. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 3: Minimum receiver antenna diameter as a function of CPI length. . . . . . 7

Figure 4: Required antenna dimensions for detection of a 0.01m2 target at a

receiver ground range of 30 km, for various values of peak transmitter

power. Transmitter and receiver antennas are square planar arrays. . . . . 8

Figure 5: Doppler spectrum of upwind sea clutter, based on Walker model. . . . . 11

Figure 6: Doppler spectrum of downwind sea clutter, based on Walker model. . . . 11

Figure 7: Geometry of bistatic radar, illustrating target velocity vector V, bistatic

angle β, and angle δ between velocity vector and bisector of bistatic angle. 12

Figure 8: Detectable geometries for bistatic concept using analytical MDV. . . . . 13

Figure 9: Prediction of clutter Doppler and range extent when the transmitter

azimuth angle is 0˚ and velocity is 50 m/s. . . . . . . . . . . . . . . . . . 14

Figure 10: Top level of RLSTAP workspace for UAV radar lineup. . . . . . . . . . . 16

Figure 11: Expanded view of Initialization glyph. . . . . . . . . . . . . . . . . . . 16

Figure 12: Expanded view of PRI Loop glyph. . . . . . . . . . . . . . . . . . . . . 17

Figure 13: Site-specific clutter model for littoral region near Maine. . . . . . . . . . 18

Figure 14: σ0 as a function of grazing angle, when λ = 0.03m, Φ=90˚, τ = 2µs,

and γa = 0.0265 rad. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 15: Top-down view of missile trajectory through rectangular detection barrier. 23

Figure 16: Top-down view of UAV radar configuration for ship self-defence. . . . . 23

Figure 17: Range-Doppler map of returns from Scenario A with littoral

background and Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 18: Range cross-cut showing Doppler magnitude of clutter and target

response for Scenario A with littoral background and Sea State 3. . . . . 27

DRDC Ottawa TR 2008-340 ix

Figure 19: STAP improvement factors for Scenario A with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 20: STAP improvement factors for Scenario A with sea clutter background

and Sea State 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Figure 21: Comparison of Adapt-then-filter improvement factors for various

clutter backgrounds for Scenario A. . . . . . . . . . . . . . . . . . . . . 28

Figure 22: Comparison of Factored STAP improvement factors for various clutter

backgrounds for Scenario A. . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 23: Range-Doppler map of radar returns from Scenario B with littoral


Figure 24: STAP improvement factors for Scenario B with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 25: STAP improvement factors for Scenario C with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 26: Detectable geometries for bistatic concept using MDV derived from

simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 27: Top-down view of area air defence, configuration 1. . . . . . . . . . . . 33

Figure 28: Range-Doppler map of returns from Scenario D with littoral


Figure 29: STAP improvement factors for Scenario D with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34


clutter backgrounds for Scenario D. . . . . . . . . . . . . . . . . . . . . 34


backgrounds for Scenario D. . . . . . . . . . . . . . . . . . . . . . . . . 35

Figure 32: STAP improvement factors for Scenario E with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Figure 33: STAP improvement factors for Scenario F with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Figure 34: Top-down view of area air defence, configuration 2. . . . . . . . . . . . 38

x DRDC Ottawa TR 2008-340

Figure 35: Range-Doppler map of returns from Scenario G with littoral


Figure 36: STAP improvement factors for Scenario G with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39


clutter backgrounds for Scenario G. . . . . . . . . . . . . . . . . . . . . 40


backgrounds for Scenario G. . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 39: STAP improvement factors for Scenario H with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 40: STAP improvement factors for Scenario I with littoral background and

Sea State 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 41: The Legend small boat and CAD model. . . . . . . . . . . . . . . . . . 42

Figure 42: Coordinate system for bistatic RCS modelling. . . . . . . . . . . . . . . 43

Figure 43: SIR versus antenna width for a square antenna, P = 1.5kW , Rg = 10km. . 47

Figure 44: SIR versus ground range for P = 1.5kW and a 0.5 m by 0.5 m antenna. . 48

Figure 45: SIR versus peak power for Rg = 10km and a 0.5 m by 0.5 m antenna. . . 48

Figure 46: SIR versus wave height for P = 1.5kW , Rg = 10km and a 0.5 m by 0.5

m antenna. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Figure 47: Range-Doppler map of returns from Scenario 1. . . . . . . . . . . . . . 51

Figure 48: STAP improvement factors for Scenario 1. . . . . . . . . . . . . . . . . 52




Figure A.1: Bistatic RCS in dBsm when the transmitter location is specified by

θi=10˚ and φi=0˚. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59


θi=30˚ and φi=0˚. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

DRDC Ottawa TR 2008-340 xi


θi=60˚ and φi=0˚. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60


θi=89˚ and φi=0˚. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Figure A.5: RCS statistics for 10 GHz, VV polarization. Monostatic RCS values

are indicated by asterisks. . . . . . . . . . . . . . . . . . . . . . . . . . 62

xii DRDC Ottawa TR 2008-340

List of tables

Table 1: Radar system parameters for over-the-horizon cruise missile detection. . 6

Table 2: Walker model parameters for X-band radar data. . . . . . . . . . . . . . 10

Table 3: Simulation scenarios for ship self-defence configuration. . . . . . . . . . 24

Table 4: Simulation scenarios for area air defence, configuration 1. . . . . . . . . 31

Table 5: Simulation scenarios for area air defence, configuration 2. . . . . . . . . 36

Table 6: Monostatic RCS of the Legend small boat, for transmitter locations

specified by θi and φi. . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Table 7: Radar system parameters for small boat detection. . . . . . . . . . . . . 45

Table 8: Simulation scenarios for UAV radar detection of small boats. . . . . . . . 50

Table 9: Minimum detectable velocity (MDV) and usable Doppler space

fraction (UDSF) for small boat detection scenarios. . . . . . . . . . . . . 51

Table 10: Payload description for the Raytheon SeaVue radar. . . . . . . . . . . . 54

Table 11: Payload estimate for the bistatic radar for cruise missile detection. . . . . 55

Table 12: Payload estimate for the monostatic radar for small boat detection. . . . . 56

DRDC Ottawa TR 2008-340 xiii


xiv DRDC Ottawa TR 2008-340

1 Introduction

In naval operations, uninhabited aerial vehicles (UAV) have the potential to provide auxil-

iary sensors to ship-based sensors. The use of UAV radar is promising, but the design of

such a system and the resulting performance are unknown. In this report, the use of UAV

radar is considered for two distinct naval surveillance requirements: 1) the over-the horizon

detection of cruise missiles, and 2) the detection of small boat threats.

The first requirement is for increased warning time against low-altitude cruise missile

threats that originate from beyond the horizon. Such threats are particularly dangerous

when they operate at high velocities and thus result in very short reaction times for a ves-

sel’s surveillance system once the threats appear on the horizon. When operating in the

littoral, naval vessels may encounter threats launched from other ships or land-based ve-

hicles. The aim of the first part of this work is to develop an affordable UAV-based radar

system that will provide over-the-horizon detection capability.

There are two general scenarios of interest for cruise missile defence, the blue water sce-

nario and the littoral scenario. In the blue water scenario, a vessel is located in the middle

of open ocean. Land-based threats are not present, and in general there is not a lot of naval

traffic in these areas. The primary source of interference for radar detection is sea clutter

at various sea states. In the littoral scenario, a vessel is up to 100 km from shore. Threats

may originate from other naval vessels or land vessels. Significant amounts of coastal

traffic may be present. Both sea and land clutter may provide sources of interference for

radar detection.

A UAV-based early detection system has several potential functionalities. It could be used

as a barrier search detection scheme. If a target is detected within the barrier, the UAV

system would cue the shipborne radar regarding an imminent threat. It could also provide

tracking capability or be used to provide illumination for missile guidance. In this study

attention is restricted to the barrier search detection capability.

The shipborne radar horizon for a Canadian Patrol Frigate is approximately 20 to 25 km.

This value was determined from empirical evidence and verified by theory. In this study,

monostatic and bistatic UAV radar systems will be designed to detect threats at a range of

25 km and beyond.

The second naval requirement involves the defence of large naval vessels against small boat

threats. These threats may be loaded with explosives which can inflict significant damage

on a ship. Small boat threats range in size from jet skis to larger pleasure craft and may be

travelling at any speed. In order to contend with these threats, it will be necessary to detect

and track them and determine their intent, at a range of 5 to 10 km from the ship.

Previous work has shown that a target with 10 m2 radar cross section (RCS) in Sea State 1

can be detected with a navigation radar at a range of 5 to 10 km [1]. However, the detection

DRDC Ottawa TR 2008-340 1

of smaller targets in higher sea states remains a challenging problem. In general, shipborne

radars are unable to detect small boats with 1 m2 RCS. The goal of the second part of this

study is to develop a UAV radar system that will detect small boats.

In this report, the design and performance analysis of a UAV radar for over-the-horizon

cruise missile detection are presented in Sections 2-6. In Section 2 an overview of current

cruise missile threats is presented. Section 3 specifies the bistatic radar concept that will

be modelled and discusses tradeoffs in parameter selection. In Section 4 a theoretical

analysis of the Doppler extent of sea clutter is carried out. Section 5 presents an overview

of RLSTAP and the space-time adaptive processing techniques that will be used in this

study. In Section 6 the performance of the bistatic radar concept is studied using RLSTAP

simulations for ship self-defence and area air defence configurations.

UAV radar has not widely studied in the literature. Previous studies have considered the

use of SAR imaging from UAV platforms using millimeter wave radar [2], [3]. Tsunoda et

al. [4] describe Lynx, a SAR/MTI radar for UAV platforms that operates at Ku-band. Lynx

is produced by General Atomics and does not carry out clutter cancellation to decrease

minimum detectable velocity. The use of bistatic UAV configurations and the use of clutter

cancellation techniques has not been previously considered.

In Sections 7-9 the design and performance of a UAV radar for small boat detection are

given. Section 7 presents an analysis of the monostatic and bistatic RCS of the Legend

small boat. In Section 8 various tradeoffs in UAV radar design are considered. Section 9

examines the performance of a monostatic UAV radar for small boat detection using RL-

STAP simulation. UAV payload considerations are discussed in Section 10. Conclusions

are presented in Section 11.

2 DRDC Ottawa TR 2008-340

2 Cruise Missile Threats

In both the blue water and littoral scenarios, a naval vessel may encounter a number of

low-altitude threats that originate from beyond the horizon, including anti-ship missiles,

cruise missiles and low-flying aircraft. This section describes these threats.

An ever-present threat to naval vessels are anti-ship missiles (ASM). Generally, ASMs are

capable of cruising at low altitudes, travelling at subsonic or supersonic speeds, and can be

launched from a ship, a land vessel or an aircraft. There are numerous types of ASMs in

production, and they are used by almost every nation in the world. One example of an ASM

is the Russian SS-N-22 ‘Sunburn’, also called the ‘3M80’ or ‘Moskit’ [5]. The Moskit is a

surface-to-surface missile with inertial guidance, a dual mode active/passive radar terminal

seeker, and an electronic protection measure (EPM) capability. With a launch weight of

4,500 kg, the Moskit has a length of 9.74 m, a body diameter of 0.76 m and a wing span of

2.1 m. When cruising at low-altitudes, the Moskit has a range of approximately 150 km

and a cruise speed of approximately Mach 2.1. Another example of an ASM is Boeing’s

AGM-84 Harpoon [6], which has an air-launched model and a surface and submarine-

launched model. The Harpoon is lighter and smaller than the Moskit, with a weight of

519 kg, a length of 3.8 m, a body diameter of 0.34 m, and a wing span of 0.83 m. At low

altitudes, the Harpoon has a range of up to 124 km with a maximum speed of Mach 0.85.

Cruise missiles are surface-to-surface missiles that typically travel at subsonic speeds. One

common example is the Tomahawk cruise missile, which has a length of 6.25 m, a diameter

of 0.51 m, and a wing span of 2.62 m. The Tomahawk has a weight of 1,450 kg, a range of

1,300 km and a maximum speed of Mach 0.75 [7]. Previous work has been carried out on

analyzing the monostatic and bistatic radar cross section (RCS) of a cruise missile [8].

Anti-ship missiles and cruise missiles are especially challenging threats because of their

high velocity and low RCS. Low-flying aircraft are also threats of interest, because they

can be used as vehicles for delivering cruise or anti-ship missiles.

This study focuses on the anti-ship missile and cruise missile threats. The radar concepts

proposed will be evaluated against a generic missile target that has an RCS of 0.1 m2 with a

velocity of Mach 0.75. These characteristics are representative of a subsonic cruise missile.

An important goal of this work is to increase the reaction time against cruise missile threats.

It is assumed that the shipborne radar detects missile threats at a range of 20 km. Detecting

a cruise missile beyond the ship’s horizon increases the detection range and the reaction

time for the ship.


3 Bistatic Radar Concept

In designing UAV radar concepts, both monostatic and bistatic concepts may be considered.

Generic monostatic and bistatic concepts are shown in Figure 1. In the illustrations, the

UAVs shown are fixed-wing aircraft. In reality, the platform hosting the radar could be a

fixed-wing aircraft, helicopter, or tethered balloon. The UAV platform may be in motion,

as is the case for a fixed-wing aircraft or helicopter, or it may be stationary, as is the case

for a hovering helicopter or tethered balloon. The choice of platform will have advantages

and disadvantages with regards to the survivability and complexity of the UAV and will not

be considered in this report.

Figure 1: Illustration of monostatic and bistatic UAV radar concepts.

The UAV radar attempts to detect threats within a rectangular barrier near the sea surface.

The barrier is located 25 to 30 km from the ship. If a single detection is generated, this

detection can be used to cue the shipborne radar. If multiple detections of the same target

are generated, then it may be possible to initiate a target track, which could then be handed

off to the shipborne radar.

In a monostatic system, the radar only requires one line-of-sight to the target and may be


designed to have a multifunction capability. However, a multifunction radar will generally

result in a large UAV payload. The goal of reducing the UAV payload leads to the consider-

ation of bistatic UAV concepts. In a bistatic system, a UAV transmitter is deployed beyond

the horizon, in the proximity of the anticipated threat. The UAV receiver would be located

directly above the ship. One advantage of such a bistatic system is that the closer prox-

imity of the transmitter to the target enhances signal-to-noise ratio. Another advantage

is that such a system may allow for simpler and cheaper UAV payloads, especially at the

transmitter. The reduction in transmitter range may also reduce the power requirements at

the transmitter. However, such a bistatic system would require synchronization between

the transmitter and receiver to achieve coherent processing gains. Furthermore, a bistatic

system requires two lines-of-sight to the target.

A number of monostatic and bistatic concepts were proposed and analyzed in [9]. For

each concept the tradeoff between radar parameters was studied, and the performance of

the various radar concepts was compared. As a result of the analysis it was determined

that a bistatic radar concept had the best tradeoff between performance and payload size

and therefore is the concept considered for this study. The bistatic radar concept and its

analysis are summarized in the following.

The bistatic concept has a receiver located above the ship and a transmitter deployed in

the direction of the barrier. The transmit antenna scans, in azimuth only, the designated

barrier. The scanning may be done mechanically or electronically, and it is assumed that

the antenna steers up to ± 60˚ in azimuth. Steering in azimuth only reduces the complexity

of the transmitter and may result in a smaller and lighter UAV payload. The inner edge of

the barrier is 5 km in ground range from the transmitter, and the barrier has a width of 5 km.

There are a number of radar parameters that may be traded off while maintaining desired

radar detection performance. It is assumed that probability of detection (Pd) is 0.9 and

probability of false alarm (Pf a) is 10−6. Table 1 gives the bistatic radar system parameters

used in this study.

The signal-to-noise ratio (SNR) for a bistatic radar with coherent integration is given by

the bistatic radar range equation.

S

N= NP

PτGT σAe

(4π)2R2T R2

RkT0BFnLs

. (1)

From this equation, various parameter values that achieve a given SNR can be computed.

Figures 2 to 4 illustrate the trade-offs involved in selecting parameters to detect cruise

missile threats. In Figure 2 all radar parameters, except for the diameter of the receiver

antenna and receiver ground range, are held constant. The plots show the minimum receiver

diameter required as a function of receiver ground range. Since SNR is quadratic in both

receiver diameter and ground range, the plots are linear. Figure 3 shows the minimum

receiver diameter as a function of coherent processing interval (CPI) length, or NP. It is

assumed that the radar signal processor performs coherent integration in order to enhance


Table 1: Radar system parameters for over-the-horizon cruise missile detection.

Parameter Definition Value

P peak power (W) 1,500

τ duty cycle 0.4

GT transmitter antenna gain

σ target RCS

Ae effective area of receiver antenna

RT transmitter-to-target range, or transmitter range

RR receiver-to-target range, or receiver range

k Boltzmann’s constant (Ws/˚K) 1.38×10−23

T0 noise temperature (˚K) 290

Fn noise figure (dB) 4.3

Ls system losses (dB) 5.0

NP number of pulses 64

σ0 clutter cross-section per unit area (m2/m2)

AC area of clutter cell

PRF pulse repetition frequency (kHz) 100

SNR. Finally, Figure 4 illustrates the trade-offs between transmitter and receiver diameter,

for various values of peak power. In general there are numerous trades that can be made

among the various radar parameters. It is assumed that the radar receiver payload is larger

than that of the transmitter.

The discussion so far has focused on a single barrier. It is straightforward to extend the

concept to multiple barriers. If these barriers are all located within an azimuthal angular

extent of 120˚ then it may be possible to use a single UAV receiver above the ship, with

multiple transmitters deployed and dedicated to each barrier region. This assumes that

the receiver scans ±60˚ in azimuth. If the receiver has multiple receive channels, then

beamforming could be used to estimate the angle of arrival for each detected threat.


25 30 35 400.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Receiver ground range (km)

Re

ce

ive

r a

nte

nn

a d

iam

ete

r (m

)

Target RCS = 0.01 m2

Target RCS = 0.1 m2

Figure 2: Minimum receiver antenna diameter as a function of receiver ground range.

0 200 400 600 800 1000 1200 1400 1600 1800 20000

0.2

0.4

0.6

0.8

1

CPI length

Re

ce

ive

r a

nte

nn

a d

iam

ete

r (m

)

Target RCS = 0.01 m2

Target RCS = 0.1 m2

Figure 3: Minimum receiver antenna diameter as a function of CPI length.


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Length of receiver antenna (m)

Length

of tr

ansm

itte

r ante

nna (

m)

0.5 kW

1.0 kW

1.5 kW

2 kW

Figure 4: Required antenna dimensions for detection of a 0.01m2 target at a receiver

ground range of 30 km, for various values of peak transmitter power. Transmitter and

receiver antennas are square planar arrays.


4 Sea clutter modelling and prediction

In this section, the bistatic concept is further analyzed by estimating the clutter Doppler

spectrum. Using these clutter Doppler estimates allows for the estimation of the minimum

detectable velocity (MDV). The MDV is the velocity of the slowest moving target that

can be detected by the radar. We begin by estimating the Doppler extent of sea clutter

for a bistatic radar with stationary platforms. In Subsection 4.1 the Walker model for the

Doppler spectrum of sea clutter is reviewed. This leads to predictions for the MDV of

targets in sea clutter, which are presented in Subsection 4.2.

4.1 Walker model for sea clutter Doppler spectrum

Walker has developed an analytical model for the Doppler spectrum of monostatic sea

clutter when the transmitter and receiver are stationary [10], [11]. The model is motivated

by data collected in a large wind-wave tank and by data collected from a cliff-top radar

experiment. In this model it is assumed that clutter returns are the result of components

caused by Bragg scattering, whitecap scattering, and sea spikes. Bragg scattering is caused

by reflections from smaller capillaries on the sea surface. Whitecap scattering results from

the gross movement of waves on the sea surface. Sea spikes are brief phenomena that

occur just before a wave crests. Each component of the clutter spectrum has a Gaussian

lineshape. The clutter Doppler spectrum in Hertz for vertical polarization, ΨV (v), and for

horizontal polarization, ΨH(v), are given by

ΨV (v) = BV exp

(

(v− vB)2

w2B

)

+W exp

(

(v− vG)2

w2W

)

,

ΨH(v) = BH exp

(

(v− vB)2

w2B

)

+W exp

(

(v− vG)2

w2W

)

+Sexp

(

(v− vG)2

w2S

)

,

where vB and vG are the center frequencies, wB,wW , and wS are the spectral widths, and

BV ,BH ,W,and S are the component magnitudes. In each expression the first component is

due to Bragg scattering and has a magnitude (BV or BH) that depends on polarization. The

second expression is due to whitecap scattering that is independent of polarization. For

horizontal polarization only, there is a third component caused by sea spikes.

In [11], parameters of the Walker model were matched to the Doppler distribution of X-

band sea clutter data collected by a stationary monostatic radar. For the data collection, the

radar was located at a height of 64 m above the mean sea level at a grazing angle of 3.6˚.

The radar waveform was a 500 MHz linear frequency modulation (FM) chirp centered at

9.75 GHz. The values of the Walker model parameters that matched the sea clutter data

are given in Table 2. Note that in the downwind case, the sea spike component was set to

zero in [11], because this component was absent from the measured data. Figures 5 and 6


illustrate the Doppler spectra of monostatic sea clutter in upwind and downwind cases. In

the upwind case, the radar line-of-sight from the radar is the opposite of the wind direction.

In the downwind case, the radar line-of-sight from the radar is the same direction as the

wind direction.

Based on these estimates, it is reasonable to assume that in general monostatic sea clut-

ter Doppler spectra occupy the frequency band from -300 Hz to 300 Hz. Assessing the

Doppler spectrum of bistatic sea clutter is an unsolved and difficult problem. The Doppler

spectrum is likely to vary significantly with bistatic angle, sea state, and look angles from

the transmitter and from the receiver. In this study, it will be assumed that the bistatic

sea clutter Doppler spectrum also occupies the frequency band from -300 Hz to 300 Hz.

In the following analysis of minimum detectable velocity, we shall be conservative and

assume that no clutter cancellation is carried out by the radar signal processor, and that

targets whose Doppler frequencies fall within the frequency band of the clutter cannot be

detected. Moreover, we shall see that clutter cancellation is not really necessary, as targets

of interest are going to be exo-clutter if the transmitter and receiver are stationary.

Table 2: Walker model parameters for X-band radar data.

Quantity Upwind Value Downwind Value

BH/BV (dB) -15.1 -14.0

W/BH (dB) 11.5 0.2

S/BH (dB) 12.8 -

vB (Hz) 54 -15.3

vG (Hz) 178 -63.9

wB (Hz) 47.5 40.4

wW (Hz) 66.0 74.11

wS (Hz) 74.5 -

4.2 Prediction of minimum detectable velocity for

targets in sea clutter

For a bistatic radar with stationary transmitter and receiver, the Doppler shift f caused by

a moving target is given by [12]

f =2V

λcosδcos(β/2), (2)

where V is the magnitude of the target velocity vector, λ is the wavelength, β is the bistatic

angle, and δ is the angle between the target velocity vector and the bisector of the bistatic

angle. Figure 7 illustrates the geometry of a bistatic radar. From (2) the bistatic radial


−500 −400 −300 −200 −100 0 100 200 300 400 5000

5

10

15

20

25

30

35

frequency (Hz)

rela

tive p

ow

er

(dB

)

V polarization

H polarization

Figure 5: Doppler spectrum of upwind sea clutter, based on Walker model.

−500 −400 −300 −200 −100 0 100 200 300 400 5000

5

10

15

20

25

30

frequency (Hz)

rela

tive p

ow

er

(dB

)

V polarization

H polarization

Figure 6: Doppler spectrum of downwind sea clutter, based on Walker model.


velocity is given by V cosδcos(β/2). Note that this is a generalization of the special case

of monostatic radar, where β = 0 and δ is the angle between the radar line-of-sight and the

target velocity vector.

At X-band (10 GHz), for bistatic sea clutter with a maximum Doppler frequency of 300 Hz,

the minimum detectable velocity (MDV) is 4.5 m/s. For a bistatic radar, the radial velocity

of the target is a function of the magnitude and direction of the target’s velocity vector and

of the bistatic angle. For example, for a target with a velocity magnitude of 250 m/s, the

target is not detectable when |cosδcos(β/2)| ≤ 0.018. The values of δ and β for which this

inequality holds is shown in the area between the two black lines in Figure 8. For bistatic

angles less than 140˚, a 250 m/s target is detectable for all values of δ, except for a small

range of values near 90˚. If the locations of the transmitter and receiver are such that the

bistatic angle for all targets in the barrier is 90˚ or less, then only targets whose velocity

vectors result in a δ between 88˚ and 92˚ are not detectable. Note that targets with these

velocity vectors have a bearing of at least 45˚ away from the receiver. In other words, all

targets in the barrier that are travelling in the direction of the receiver, and thus pose the

most immediate threat to the receiver, are detected by the bistatic concept. Therefore, the

radar would not need to use clutter cancellation to detect targets under most geometries.

Figure 7: Geometry of bistatic radar, illustrating target velocity vector V, bistatic angle β,

and angle δ between velocity vector and bisector of bistatic angle.

For a transmitter and receiver that are in motion, the platform motion adds significant

spread to the clutter and increases the minimum detectable velocity. Theoretical predic-

tions for the range-Doppler spread of clutter due to platform motion have been developed

in [13]. For a transmitter and receiver each travelling with a velocity of 50 m/s, Figure

9 shows the prediction for mainlobe clutter. The blue lines show the edge of the trans-

mitter mainbeam, while the red lines show the edge of the receiver mainbeam. Due to

the high PRF waveform, there are multiple range ambiguities. Both the transmitter and

receiver mainbeams are shown, and it is assumed that the clutter will be strongest in the

areas where the mainbeams intersect. From Figure 9 it is estimated that the clutter Doppler


0 20 40 60 80 100 120 140 160 1800

20

40

60

80

100

120

140

160

180

bistatic angle (deg)

delta (

deg)

Moving platforms (50 m/s)

Stationary platforms

Figure 8: Detectable geometries for bistatic concept using analytical estimates for MDV.

The area between the two black lines indicates the values of bistatic angle and δ for which

a cruise missile of velocity 250 m/s is not detectable and stationary transmitter and receiver

platforms. The two blue lines correspond to the transmitter and receiver each travelling at

a velocity of 50 m/s.


spectra occupies the frequency band from -8 kHz to 8 kHz. This prediction indicates that

the clutter Doppler occupies a frequency bandwidth of approximately 15 to 20 kHz, which

is approximately 27 times the bandwidth of 600 Hz for a stationary transmitter and receiver.

Assuming that the clutter Doppler spectra occupies a frequency band from -8 kHz to 8

kHz and that the transmitter and receiver are each travelling with a velocity of 50 m/s, the

MDV is 120 m/s. For a target with a velocity magnitude of 250 m/s, the target is not

detectable when |cosδcos(β/2)| ≤ 0.48. The values of δ and β for which this inequality

holds is shown in the area between the two blue lines in Figure 8. When the bistatic angle

is greater than approximately 120˚, no targets are detectable. For bistatic angles less than

60˚, the target is detectable only when δ is less than 60˚ or greater than 120˚.

−50 0 500

0.5

1

1.5

2

2.5

3

Doppler frequency (kHz)

range (

km

)

Transmitter mainbeam

Receiver mainbeam

Figure 9: Prediction of clutter Doppler and range extent when the transmitter azimuth

angle is 0˚ and velocity is 50 m/s.


5 Simulation and Processing

The UAV radar concepts developed in this study were modelled using RLSTAP, a high-

fidelity software simulation tool developed by the Sensors Directorate at Air Force Re-

search Laboratory in Rome, NY. RLSTAP is an end-to-end simulation tool that allows for

the development and analysis of radar systems and radar signal processing algorithms. In

this study, RLSTAP was used to generate simulated radar data, and space-time adaptive

processing (STAP) algorithm development, coding and testing was done in Matlab.

5.1 RLSTAP

RLSTAP was developed under the Khoros software development environment and models

various radar system tasks and processes. Each task or process is described by software

code and is represented in the GUI as a glyph. A number of pre-defined glyphs are included

with RLSTAP, and the package allows the user to create new glyphs as well. A radar

system is simulated in RLSTAP by assembling a group of glyphs and connecting them

appropriately as a network of glyphs, also called a lineup. Because of the general nature of

the processes represented by glyphs, the simulation tool is able to simulate a wide variety of

airborne, spaceborne or ground based multi-channel radars. This allows a user to analyze

the performance of advanced radar systems and advanced signal processing techniques.

RLSTAP also allows a user to easily alter system parameters or processing techniques and

immediately see the effect of these changes on system performance.

A powerful feature of RLSTAP is its high fidelity clutter models. Realistic clutter mod-

els are vital to producing simulated data which can be used to accurately predict the per-

formance of signal processing algorithms. The software allows for the inclusion of both

clutter and jamming as interference, although in this study only clutter interference is con-

sidered. In general, the user can specify either homogeneous clutter models or realistic

clutter returns that use site-specific terrain data. Site-specific clutter is obtained from digi-

tal elevation maps (DEM’s) and land-use-land-clutter (LULC) files. See [14] for a detailed

discussion.

Figure 10 shows the RLSTAP lineup used to model a radar system. The glyph labelled

“Initialization” is an encapsulated glyph that itself represents a network of glyphs, shown

in Figure 11. The Initialization glyph establishes the details of the transmitter, receiver

and target by specifying the location, velocity, and orientation (that is, pitch, yaw and roll)

of the various platforms. Also specified in this glyph are the size, structure and antenna

patterns of the transmitter and receiver antennas. The target RCS is defined by the user in

this glyph.

In the main lineup, the “Sigma0 LUT” glyph generates a look-up table of scattering coef-

ficients as a function of grazing angle for various clutter surface types. The “Simulation


Figure 10: Top level of RLSTAP workspace for UAV radar lineup.

Figure 11: Expanded view of Initialization glyph.


Controller” glyph allows the user to specify various simulation parameters, such as the sim-

ulation sampling rate, minimum and maximum simulation range, and the number of pulses

per coherent processing interval. The “HomoScat” glyph specifies the clutter background

that is to be used in the simulation. The “Tx Waveform Generator” glyph generates a phase

coded transmitter waveform.

The glyph labelled “PRI Loop” is an encapsulated glyph that simulates the performance

of the physical channel and the radar receiver. Figure 12 shows the components of this

encapsulated glyph. The “Physical Model” glyph generates clutter and target returns and

performs range folding, whereby radar returns are made range ambiguous, according to the

chosen PRI. In the “Receiver Model” glyph noise is added to the waveform and filtering

is performed. The “Coherent Signal Processing” glyph then performs pulse compression

of the received waveform.

Figure 12: Expanded view of PRI Loop glyph.

The output of “PRI Loop” is a range-channel-pulse datacube. RLSTAP features a limited

number of signal processing glyphs which can be used to process datacubes. However, al-

gorithm implementation and development is more easily carried out in Matlab. As a result,

in this study, RLSTAP datacubes are exported to Matlab and processed using algorithms

written in Matlab code.

A site-specific model of the area off the coast of Maine was used in the simulation, as shown

in Figure 13. In this figure, the transmitter and transmit antenna mainbeam are represented

by the blue triangle and the blue lines. The green triangle and green lines represent the

receiver platform and receiver antenna mainbeam, respectively.


Figure 13: Site-specific clutter model for littoral region near Maine.

In order to enhance simulation fidelity, the Georgia Institute of Technology (GIT) sea clut-

ter model [15] was implemented in RLSTAP and was utilized in this study. In the GIT

model the radar reflectivity σ0 of sea clutter is given by

σ0 = 10log(

3.9×10−6λγ0.4AiAuAw

)

, where

Ai =σ4

Φ

1+σ4Φ

,

Au = exp(

0.2cosΦ(1−2.8γ)(λ+0.015)−0.4)

,

Aw =

(

1.94Vw

1+Vw/15.4

)qw

,

σΦ = (14.4λ+5.5)γhav

λ,

Vw = 8.67h0.4av ,

qw =1.1

(λ+0.015)0.4,

and where γ is the grazing angle, hav is the average wave height, Φ is the angle between

boresight and the wind direction, and λ is the radar wavelength. A full explanation of the

model and its parameters can be found in [15]. This model has been validated for values of

average wave height between 0 and 4 meters, and for grazing angles between 0˚ and 10˚. It

is noted that the GIT model quantifies the conditions of the sea using average wave height.

However, it is common in radar studies to describe sea conditions using the sea state. This

study will consider Sea States 3 and 5. To model Sea State 3, an average wave height of

0.7 m is used in the GIT model. For Sea State 5, an average wave height of 2 m is used.

Figure 14 shows σ0 as a function of grazing angle, when λ = 0.03 m, Φ = 90˚, pulse width

τ = 2 µs, and antenna beamwidth γa = 0.0265 rad. For grazing angles between 10˚ and 30˚,


the values of σ0 are approximately constant and independent of grazing angle. Therefore,

it is assumed that the value of σ0 for grazing angles between 10˚ and 30˚ is the value of σ0

for a grazing angle of 10˚.

10−1

100

101

−65

−60

−55

−50

−45

−40

−35

−30

−25

Grazing angle (deg.)

σ0 (

dB

)

hav

=4 m

hav

=3 m

hav

=2 m

hav

=1 m

Figure 14: σ0 as a function of grazing angle, when λ = 0.03m, Φ=90˚, τ = 2µs, and

γa = 0.0265 rad.

5.2 Space-Time Adaptive Processing

In this section we discuss space-time adaptive processing (STAP) of the output data from

the ‘PRI Loop’ glyph. This data has been down converted, matched filtered and pulse

compressed, assuming a 13-bit Barker code on the transmitted waveform. As a result,

the output of the ‘PRI Loop’ glyph is a three-dimensional data cube of complex samples

which are indexed by range, channel, and pulse. Consider M pulses, N channels, and a

fixed range gate. Define the MN-dimensional data vector x, where

x =

x0

x1...

xM−1

, (3)

and each xi is a N-dimensional vector of returns from N channels for pulse i. A STAP pro-

cessor takes the data vector x as input and considers whether a target is present at Doppler

frequency fd . The output is a (scalar) test statistic z, which is passed to a threshold detector.

The threshold detector compares z to a threshold and makes a target detection decision.


Let the data vector be given by (3). STAP constructs an MN-dimensional linear filter w

and computes the test statistic z = w · x. The data vector x can be factored as the sum of

the returns due to signal and interference. That is, x = xt +xI , where xt is the signal return

and xI is the interference and noise return that may include clutter, receiver noise and noise

jamming. STAP computes the linear filter that maximizes the signal-to-interference-plus-

noise ratio (SINR) of the filtered data vector. The optimal STAP filter is

w = argmaxy

|y · xt ||y · xI|

, (4)

that is, w is the vector that maximizes the quotient|y·xt ||y·xI | over all choices of y.

For the case of homogeneous interference with a known covariance matrix R = E[xIx∗I ], it

was shown [16] that the optimal filter is given by

w = R−1s, (5)

where s is the target steering vector. The target steering vector is defined to be the MN-

dimensional vector with the same phase as the expected return from the target only, but with

normalized magnitude. It will be assumed that (5) is optimal, even when the interference is

not homogeneous. In realistic environments, R is not known and must be estimated. In this

study, R is estimated by using a secondary data set (SDS) in nearby range cells. Sample

matrix inversion (SMI) then estimates R−1 by taking the inverse of the sample matrix.

Fully Adaptive STAP processes an NM-dimensional data vector, consisting of returns from

all N channels and all M pulses. For even moderate values of N and M, Fully Adaptive

STAP is too computationally intensive for real-time applications. Furthermore higher di-

mensions of R require larger SDSs to provide good covariance estimates. However, in non-

homogeneous environments, interference characteristics can vary widely throughout larger

SDSs. This motivates the search for reduced dimension methods, where one processes

fewer pulses and channels simultaneously. These methods are called partially adaptive

STAP methods, require less processing complexity and allow for the use of smaller SDSs.

Partially adaptive STAP is much more amenable to real-time applications. These methods

process data from less than N channels and/or less than M pulse returns at a time. As a re-

sult, the inverses of much smaller dimension matrices need to be computed. Furthermore,

the secondary data sets used for covariance estimation can be smaller, which is advanta-

geous in non-homogeneous interference environments. We now give a brief outline of the

partially adaptive STAP methods used in this study, Adapt-then-filter STAP and Factored

STAP.

Adapt-then-filter STAP (also called element space pre-Doppler STAP in [17]) performs

adaptive filtering of a small number of pulses, K, followed by Doppler filtering. Adapt-

then-filter STAP operates in a sliding window fashion. Specifically, Adapt-then-filter STAP

forms K-dimensional data sub-vectors


xAT F,i =

xi

xi+1...

xi+K−1

, i = 0, ...,M−K. (6)

For each i, the weight vector wAT F,i = R−1AT F,i sAT F,i is computed, where RAT F,i is an estimate

of the covariance matrix of xAT F,i, and sAT F,i is a MK-dimensional steering vector, defined

as u⊗sS, where sS is the spatial steering vector and u is a K-dimensional binomial taper. For

sub-vector i, the adaptive output is yAT F,i = wAT F,i · xAT F,i. A test statistic zAT F is computed

as zAT F = f · yAT F , where yAT F = [yAT F,0 yAT F,1 ... yAT F,M−K]T and f is a Doppler filter of

length M −K + 1 corresponding to the first M −K + 1 elements of the time-only steering

vector sT .

Factored STAP (also called Post-Doppler adaptive beamforming in [17]) performs Doppler

filtering on each channel followed by adaptive filtering of all channels in each Doppler bin.

Let y1,y2, · · · ,yN be the M-dimensional data vectors for each of the N channels. Consider

Doppler bin i, and let fi be the corresponding Doppler filter of length M. The Post-Doppler

data set is given by

xF,i = [ fi · y1 fi · y2 · · · fi · yN ]T . (7)

For Doppler bin i, Post-Doppler STAP computes the weight vector wF,i = R−1F,i sF,i, where

RF,i is an estimate of the covariance matrix of xF,i, and sF,i is the pot-Doppler target spatial

steering vector. The test statistic for Doppler bin i is then given by zF,i = wF,i · xF,i.

The performance of the STAP techniques will be measured by computing the improvement

factor, which is the ratio of the SINR after processing to the SINR before processing. As

will be seen in the IF plots, the IF typically is lower for target Doppler frequencies in the

clutter notch, where the clutter Doppler power is strongest. The value of the improvement

factor near the clutter notch measures the extent to which STAP cancels clutter power while

preserving target power. The improvement factor outside of the clutter notch indicates the

ability of STAP to detect targets in noise.

STAP performance will also be measured by computing MDV and usable Doppler space

fraction (UDSF). The usable Doppler space fraction is the fraction of the Doppler space

over which a target can be detected. The MDV is determined by estimating the clutter

power after STAP processing.


6 Concept Performance

As discussed earlier, the UAV radar system is designed to provide a barrier search detection

capability. The barrier is assumed to be a rectangular region near the Earth’s surface, with

dimensions 14 km by 5 km. It is assumed that the barrier is located 25 km to 30 km

from the ship. The goal of the UAV system is to detect all cruise missile targets that cross

through the barrier. In this section the performance of the bistatic radar system is evaluated

by simulating radar scenarios in RLSTAP. Three different scenarios are considered. A ship

self-defence scenario is simulated in Subsection 6.1, and area air defence scenarios are

simulated in Subsections 6.2 and 6.3.

In order to assess UAV radar performance, a specific missile trajectory within the barrier

is assumed. For the entire trajectory, the missile is assumed to be travelling at an altitude

of 10 m with a velocity of 250 m/s. The missile trajectory has three distinct segments, as

shown in Figure 15. In the first segment, the missile travels for a distance of 2.8 km at a

45˚ angle to the long axis of the barrier. During the second segment, a distance of 5.1 km is

travelled on a straight line that is almost parallel to the long axis of the barrier. In the third

and final segment, the missile travels for a distance of 2 km on a course that is parallel to the

short axis of the barrier. The segments of the trajectory are designed to provide differing

geometries in the simulation results.

For the RLSTAP simulations, the transmit antenna is a 0.4 m-by-0.4 m side-looking, rect-

angular array. The transmitted waveform has a PRF of 100 kW with a pulse width of 3.9

µs. The high PRF waveform was chosen so that the velocity of a Mach 2 target could be re-

solved unambiguously. Coherent pulse intervals of 64 pulses are transmitted, and the peak

transmitted power is 1.5 kW. The receive antenna is a 1 m-by-3 m side-looking, rectangular

array with 3 sub-apertures and 0.4 m spacing between sub-apertures.

6.1 Ship Self-Defence

The barrier may be specified at any orientation with respect to the ship. To model a ship

self-defence scenario, the barrier is aligned as shown in Figure 16. The transmitter flies in

a racetrack near the barrier, while the receiver flies in a racetrack above the ship. Both the

transmitter and receiver antennas scan the barrier to search for targets. Note that multiple

transmitter and receiver platforms would be required to provide persistent coverage of the

barrier. That is, if it is assumed that the transmitter antenna looks to one side only, then a

single UAV can only provide barrier coverage for one leg of the racetrack. Two or more

transmitter UAVs could be used to provide persistent coverage. Similarly, if the receiver

antenna looks to one side only, multiple UAVs would be required to provide persistent

coverage.

The performance of the proposed bistatic UAV radar is evaluated by simulating the radar re-


Figure 15: Top-down view of missile trajectory through rectangular detection barrier.

Figure 16: Top-down view of UAV radar configuration for ship self-defence.


turns when the cruise missile target is halfway through each segment in its trajectory. That

is, there were three separate simulation scenarios run for the ship self-defence configura-

tion. These are summarized in Table 3. The points in the missile trajectory corresponding

to Scenarios A, B and C are shown in Figure 16.

Table 3: Simulation scenarios for ship self-defence configuration.

Target Bistatic Bistatic Bistatic

Scenario Segment Range (km) Doppler (kHz) Angle (˚)

A 1 39.1 13.5 16.5

B 2 35.1 4.6 8.8

C 3 32.0 16.7 8.0

In this configuration, the transmitter and receiver are aligned so that the bistatic angle is

generally close to zero. Thus, as is the case with monostatic radar, crossing targets with

small radial velocity can be buried in the clutter and are difficult to detect. However, these

targets are less of a threat to the ship, because they are not headed in the direction of

the ship. Higher velocity missiles pose a bigger threat because they decrease the radar’s

reaction time, but higher velocity crossing missiles have greater Doppler frequency and are

easier to detect than lower velocity missiles.

Scenario A was simulated in RLSTAP, with an illustration of the geometry shown in Fig-

ure 13. In order to produce a sea clutter background, the radar system was oriented so that

the radar footprints contained only sea clutter. To produce a littoral clutter background, the

system was oriented so that land clutter and sea clutter were contained in the radar foot-

prints. For Scenario A with a littoral clutter background and sea state 3 for the sea clutter,

the range-Doppler map shown in Figure 17. It is seen that the target return is located at

the expected bistatic range of 39.1 km, with a bistatic Doppler of 13.5 kHz. Although

the target return is much weaker than that of the clutter return, the target can be detected

because it is outside of the clutter Doppler space. The Doppler processing separates the

clutter and target in Doppler and allows the target to be detected, since the target is above

the noise floor. Figure 18 shows the Doppler magnitude at the range where target response

is strongest. Figure 19 shows the improvement factors for STAP processing. For both tech-

niques, the IF is higher for larger frequencies that are outside the clutter Doppler. At zero

Doppler, where the clutter is strongest, the IF decreases by about 30 dB. This decrease in

IF is also called the clutter notch. The Adapt-then-filter technique has better performance

than Factored STAP, especially at the smaller positive frequencies near the clutter notch.

Note that both STAP techniques have IFs that are 30 dB greater than that of Doppler pro-

cessing. This gain is due to the adaptivity that is used in Adapt-then-filter and Factored

STAP. For Doppler processing, the minimum detectable velocity is 70.3 m/s, correspond-

ing to a Doppler frequency of 4.7 kHz. For both STAP techniques, the MDV is 46.9 m/s,

corresponding to a Doppler frequency of 3.1 kHz.


Figure 19 shows STAP performance for Scenario A for littoral clutter and sea state 3.

Scenario A was also simulated for sea clutter and sea state 5. Figure 20 shows the resulting

STAP performance. In this case, ATF has better performance at all Doppler frequencies.

Furthermore, the gain in STAP performance compared to Doppler processing is 10 dB

greater than for a littoral background with sea state 3.

Under Scenario A, two different clutter types were considered, sea clutter and littoral clut-

ter. As well, two different sea states were considered, sea states 3 and 5. For all four

possible clutter backgrounds, simulations were run in RLSTAP, with results processed us-

ing STAP. Figure 21 shows the results for Adapt-then-filter processing. It is evident that

the IF is larger for the sea clutter backgrounds than for the littoral backgrounds. The littoral

background is a mixture of land and sea clutter, and the heterogeneity of the clutter results

in poorer performance compared to homogeneous sea clutter in this case. For sea clutter,

Figure 21 also indicates that changing the sea state from 3 to 5 augments the IF by 3 to 5

dB for most of the Doppler spectrum. For the littoral background, changing the sea state

has almost no effect on performance.

Figure 22 compares the performance of Factored STAP for all four clutter backgrounds.

Over most of the Doppler spectrum, Factored STAP has higher IF for sea clutter compared

to littoral clutter. For sea clutter, the performance for sea state 3 and sea state 5 is very

closely matched. However, for littoral clutter, performance is 5 to 10 dB better for sea state

3 than for sea state 5. Note that all four clutter backgrounds result in the same performance

in the clutter notch.

For Scenario B, which corresponds to the second segment of the missile trajectory, the

range-Doppler plot is shown in Figure 23. During the second segment, the missile is al-

most a crossing target relative to both the transmitter and the receiver. As a result, the

target has very little radial velocity and is partly obscured by the clutter Doppler. Figure 24

shows the improvement factors for Scenario B. Adapt-then-filter processing has better per-

formance than Factored STAP. The increase in performance of STAP processing compared

to Doppler processing is similar to that from Scenario A.

In Scenario C, the target is in the third segment of its trajectory and is heading directly

towards the ship. As a result, the target is outside of the clutter Doppler space and can be

detected without STAP processing. Figure 25 shows IFs for STAP and Doppler process-

ing for Scenario C. As was the case with Scenarios A and B, Adapt-then-filter has better

performance than Factored STAP.

Recall that Figure 8 used an analytical estimate for the Doppler extent of sea clutter to

determine the detectable geometries for specified transmitter, receiver and target velocities.

This plot showed the values for δ and β for which the resulting bistatic Doppler frequency

was greater than that of the clutter Doppler. Using the MDV values for Scenarios A, B and

C, it is evident that the Doppler extent of clutter is from -4.7 kHz to 4.7 kHz for Doppler

processing and from -3.1 kHz to 3.1 kHz for STAP processing. Figure 26 presents the


detectable geometries for a transmitter and receiver with a velocity of 50 m/s and a target

with a velocity of 250 m/s, using the values of MDV that were generated from simulations.

The results are slightly different that those in Figure 8, which shows the detectable geome-

tries for an analytical estimate of MDV. For the ship self-defence configuration, the bistatic

angle varies between 0˚ and 60˚. It is evident that after STAP processing a target can be

detected as long as δ is less than 80˚ or greater than 100˚.

Doppler (Hz)

Range (

km

)

−5 −4 −3 −2 −1 0 1 2 3 4

x 104

37.5

38

38.5

39

39.5

40

−8

−7

−6

−5

−4

−3

Figure 17: Range-Doppler map of returns from Scenario A with littoral background and

Sea State 3.


−5 −4 −3 −2 −1 0 1 2 3 4 5

x 104

10−8

10−7

10−6

10−5

10−4

10−3

10−2

Doppler (Hz)

Am

plit

ude

Figure 18: Range cross-cut showing Doppler magnitude of clutter and target response for

Scenario A with littoral background and Sea State 3.

−5 0 5

x 104

0

10

20

30

40

50

60

70

80

90

100

Doppler frequency (Hz)

Impro

vem

ent F

acto

r (d

B)

Doppler processing

Adapt−then−filter

Factored STAP

Figure 19: STAP improvement factors for Scenario A with littoral background and Sea

State 3.


−5 0 5

x 104

10

20

30

40

50

60

70

80

90

100

110


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 20: STAP improvement factors for Scenario A with sea clutter background and Sea

State 5.

−5 0 5

x 104

30

40

50

60

70

80

90

100

110


Impro

vem

ent F

acto

r (d

B)

Sea clutter, sea state 3

Littoral, sea state 3



Figure 21: Comparison of Adapt-then-filter improvement factors for various clutter back-

grounds for Scenario A.


−5 0 5

x 104

40

50

60

70

80

90

100


Impro

vem

ent F

acto

r (d

B)





Figure 22: Comparison of Factored STAP improvement factors for various clutter back-

grounds for Scenario A.

Doppler (Hz)

Ra

ng

e (

km

)

−4 −2 0 2 4

x 104

34.5

35

35.5

36

36.5

37

−9

−8

−7

−6

−5

−4

−3

−2

Target

Figure 23: Range-Doppler map of radar returns from Scenario B with littoral background

and Sea State 3.


−5 0 5

x 104

0

10

20

30

40

50

60

70

80


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 24: STAP improvement factors for Scenario B with littoral background and Sea

State 3.

−5 0 5

x 104

0

10

20

30

40

50

60

70

80


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 25: STAP improvement factors for Scenario C with littoral background and Sea

State 3.


0 20 40 60 80 100 120 140 160 1800

20

40

60

80

100

120

140

160

180

bistatic angle (deg)

delta (

deg)

Doppler processing

STAP

Figure 26: Detectable geometries for bistatic concept using MDV derived from simula-

tions. The transmitter and receiver have velocities of 50 m/s and the target has a velocity of

250 m/s. For a given bistatic angle, the target cannot be detected if its δ angle is between

the two lines.

6.2 Area Air Defence, Configuration 1

In the previous subsection, the UAV radar was used to provide ship self-defence. The

UAV radar may also be used to provide area air-defence. Figure 27 illustrates an area air

defence scenario, where Ship A is tasked with defending ship B. In this case, the radar again

attempts to detect cruise missiles that pass through a rectangular barrier in the direction of

Ship B. The barrier is beyond Ship A’s horizon and may or may not be beyond Ship B’s

horizon. This configuration is the first area air-defence configuration considered in this

report. A second configuration is considered in the next subsection.

Table 4: Simulation scenarios for area air defence, configuration 1.



D 1 35.5 8.5 56.2

E 2 34.2 -1.3 48.6

F 3 33.2 14.4 39.0

Three scenarios were simulated in RLSTAP, with each scenario corresponding to the mid-

point of a target segment. Table 4 lists the scenarios and the resulting bistatic range and


bistatic Doppler of the target. Figure 27 illustrates the points in the missile trajectory cor-

responding to Scenarios D, E and F. Compared to the ship self-defence configuration, the

simulations involve larger bistatic angles due to the radar geometry. Under all three scenar-

ios, simulation results show that the MDV was 70.3 m/s for Doppler processing and 46.9

m/s for both Adapt-then-filter and Factored STAP. Figure 28 shows the range-Doppler map

for Scenario D with a littoral clutter background and sea state 3. This scenario and Scenario

A from the ship self-defense configuration correspond to the first segment in the missile tra-

jectory. Note that for Scenario D the target return is closer to the clutter Doppler due to the

increased bistatic angle. In order to reduce the extent of clutter Doppler, STAP processing

was carried out. The resulting IFs are shown in Figure 29. It is seen that Adapt-then-filter

has better performance in the clutter notch and at lower frequencies.

For Scenario D, simulations were run for littoral and sea clutter types and for sea state 3

and sea state 5. Figure 30 compares the performance of Adapt-then-filter for all four clutter

backgrounds. It is evident that sea state has little effect on IF, both for littoral clutter and

sea clutter. The performance for sea clutter is about 20 dB better for than that for littoral

clutter. Figure 31 shows the IFs for Factored STAP for all four clutter backgrounds. In this

case, the sea clutter type has greater IFs but the difference in IF between sea clutter and

littoral clutter is less than 10 dB for most frequencies.

The IFs for Scenario E are shown in Figure 32. It is seen that Adapt-then-filter has better

performance at lower frequencies, while at higher frequencies the two STAP methods have

similar performance. In Scenario F, Adapt-then-filter has higher IFs for almost all of the

Doppler spectrum, as shown in Figure 33.

Figure 26 gives the detectable values of δ and β for area air defence, configuration 1 using

the MDV values derived from simulation. Due to the orientation of the barrier relative to

ship A, the potential bistatic angles range from 0˚ to 105˚, which is a greater range than

for the ship self defence configuration. At larger bistatic angles, the values of δ for which

targets can be detected are less. For example, for a bistatic angle of 105˚, targets with δ

less than 70˚ and greater than 110˚ can be detected with STAP processing.


Figure 27: Top-down view of area air defence, configuration 1.

Doppler (Hz)

Ra

ng

e (

km

)

−4 −2 0 2 4

x 104

34.5

35

35.5

36

36.5

37

−10

−9

−8

−7

−6

−5

−4

−3

Figure 28: Range-Doppler map of returns from Scenario D with littoral background and

Sea State 3.


−5 0 5

x 104

0

10

20

30

40

50

60

70

80

90

100


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 29: STAP improvement factors for Scenario D with littoral background and Sea

State 3.

−5 0 5

x 104

40

50

60

70

80

90

100

110

120

130


Impro

vem

ent F

acto

r (d

B)






grounds for Scenario D.


−5 0 5

x 104

30

40

50

60

70

80

90


Impro

vem

ent F

acto

r (d

B)






grounds for Scenario D.

−5 0 5

x 104

0

10

20

30

40

50

60

70

80

90


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 32: STAP improvement factors for Scenario E with littoral background and Sea

State 3.


−5 0 5

x 104

0

10

20

30

40

50

60

70

80

90

100


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 33: STAP improvement factors for Scenario F with littoral background and Sea

State 3.

6.3 Area Air Defence, Configuration 2

A second area air-defence configuration was studied, as represented by Figure 34. Once

again, the UAV radar augments the sensor capability of ship A, in order to detect threats

to ship B. Because of the geometry of this configuration, the range of bistatic angles are

greater than that for the first area air defence configuration. Table 5 shows the scenarios and

resulting bistatic range and Doppler for this configuration, where each scenario corresponds

to the midpoint of the target segment, as shown in Figure 34. Under all three scenarios,

the MDV is 70.3 m/s for Doppler processing and 46.9 m/s for both Adapt-then-filter and

Factored STAP.

Table 5: Simulation scenarios for area air defence, configuration 2.



G 1 31.2 2.1 102.8

H 2 32.3 -5.4 91.8

I 3 33.1 8.9 79.7

Figure 35 shows the range-Doppler map for radar returns from Scenario G. The target

return is buried in the clutter return, and as a result cannot be seen in the figure. This

contrasts with Scenario A and Scenario D from the ship self-defence configuration and


area air-defence, configuration 1, respectively. In those configurations, the bistatic angle

and velocity vector of the target are such that the target appears outside of the clutter. In

this case, the bistatic angle is larger, and the relative direction of the velocity vector is such

that the bistatic radial velocity is small. The improvement factors for STAP are shown in

Figure 35. It is seen that Adapt-then-filter has larger IF for most of the Doppler spectrum

and especially near the clutter notch.

Scenario G was simulated for sea clutter and littoral clutter types, and for sea state 3 and

sea state 5. Figure 37 compares the performance of Adapt-then-filter for all four clutter

backgrounds. The IFs for the sea clutter backgrounds are greater than those for the littoral

backgrounds. For Factored STAP, shown in Figure 38, the littoral backgrounds have lower

IFs compared to those for the sea clutter backgrounds.

In Scenario H, the target is almost a crossing target relative to ship B, but because of the

bistatic geometry, it has large Doppler relative to ship A. As a result, the bistatic Doppler is

large enough that it is located outside of the clutter. Figure 39 shows that Adapt-then-filter

has higher IFs than Factored STAP for this scenario. The cruise missile is heading directly

toward ship B under Scenario I, and the Doppler response of the target is outside that of

the mainlobe clutter. In Figure 40, it is seen that Adapt-then-filter and Factored STAP have

similar performance throughout most of the Doppler spectrum.

When considering detectable geometries, the relationship between δ and β is shown in

Figure 26. Due to the orientation of the barrier relative to ship A, the potential bistatic angle

ranges from 30˚ to 150˚. As bistatic angle increases, the values of δ for which the target is

detectable decrease. Because of the barrier orientation in area air defence, configuration 2,

the target Doppler response for Scenario G is buried in the clutter Doppler. For Scenarios

A and D, the bistatic angle is smaller and the targets are detectable.

6.4 Discussion

The simulation results presented in this section indicate a number of interesting conclu-

sions. There are a number of results that are common to the ship self-defence and both

area air defence configurations. For all three configurations, STAP processing has higher

improvement factors than Doppler processing. In addition, Adapt-then-filter has higher IFs

than Factored STAP in most cases. For all scenarios, the MDV is 70.3 m/s for Doppler

processing and 46.9 m/s for both Adapt-then-filter and Factored STAP. The consistency of

the MDV values is likely due to the high PRF waveform, which results in the clutter power

being concentrated in a limited number of Doppler cells near zero frequency.

The different geometries associated with the three configurations have a significant effect

on the detectability of targets. For the ship self-defence configuration, a target is not de-

tectable if it has low radial velocity in the direction of the ship. For the area air defence


configurations, the bistatic angle is larger. As a result it is possible for some crossing tar-

gets to be detected, while other targets that are heading more directly toward the ship may

not be detected. The design of an area air defence configuration will have to be carefully

considered to account for this effect.

Figure 34: Top-down view of area air defence, configuration 2.


Doppler (Hz)

Ra

ng

e (

km

)

−4 −2 0 2 4

x 104

28.5

29

29.5

30

30.5

31

−9

−8

−7

−6

−5

−4

−3

Target

Figure 35: Range-Doppler map of returns from Scenario G with littoral background and

Sea State 3.

−5 0 5

x 104

0

10

20

30

40

50

60

70

80

90


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 36: STAP improvement factors for Scenario G with littoral background and Sea

State 3.


−5 0 5

x 104

40

50

60

70

80

90

100

110


Impro

vem

ent F

acto

r (d

B)






grounds for Scenario G.

−5 0 5

x 104

40

45

50

55

60

65

70

75

80

85

90


Impro

vem

ent F

acto

r (d

B)






grounds for Scenario G.


−5 0 5

x 104

0

10

20

30

40

50

60

70

80

90


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 39: STAP improvement factors for Scenario H with littoral background and Sea

State 3.

−5 0 5

x 104

0

10

20

30

40

50

60

70

80

90

100

110


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 40: STAP improvement factors for Scenario I with littoral background and Sea

State 3.


7 Small Boat Radar Cross Section

The remainder of this report will focus on the problem of small boat detection using UAV

radar. The Maritime Force Protection (MFP) Technology Demonstration Project (TDP)

is studying force protection against the small boat threat. One component of the TDP is

examining the use of UAVs to detect small boats. DRDC Ottawa is supporting this effort

by studying radar detection using UAVs.

The “Legend” small boat, shown in Figure 41, is being used in live weapons experiments

under the MFP TDP. This boat is considered the main threat in designing a UAV radar

system to detect small boat threats. A key step in predicting radar system performance

involves the prediction of the RCS of the small boat. Such predictions of the monostatic

and bistatic RCS are presented in this section.

Figure 41: The Legend small boat and CAD model.

A CAD model of the Legend was produced and is shown in Figure 41. This document

considers a monostatic UAV concept, and the monostatic RCS measurements are relevant

to the analysis. Future work may consider the use of bistatic UAV concepts. In this case

bistatic RCS measurements would be required to carry out an analysis. The bistatic RCS

results are provided here for completeness.

The software Rapport was used to model the electromagnetic signature of the Legend

model. The RCS σ of an object is given by

σ = limR→∞

4πR2

∣

∣

∣

∣

EscatteredEincident

∣

∣

∣

∣

2

, (8)

where Eincident is the incident field strength, Escattered is the scattered field strength, and

R is the distance from the object to the location where field strength is measured. In this

modelling, the material of the boat is specified as a perfect conductor. Rapport uses a

“shooting and bouncing” ray technique to compute the scattered field strength. Specifi-

cally, a set of parallel rays are launched from the incident direction. Each ray is traced

as it bounces from one part of the object to another until it exits the object. To compute

the effect of the first bounce, Physical Optics is used, while subsequent bounces use the


Physical Theory of Diffraction. The radar frequency used is 10.0 GHz (X-band) with

VV polarization. It is assumed that the boat is located in the open ocean in Sea State 1.

The RCS measurements predicted include the direct path and multi-path reflections. The

multi-path reflections include boat-to-sea, sea-to-boat, and sea-to-boat-to-sea.

The RCS of the Legend model is predicted for a variety of transmitter and receiver posi-

tions. As shown in Figure 42, the center of the boat is placed at the origin of a three-

dimensional coordinate system. Without loss of generality, it is assumed that the primary

axis of the boat body is aligned with the x-axis. The locations of the transmitter and re-

ceiver are given in spherical coordinates, where angles θi and φi specify the transmitter

position, while θr and φr specify that of the receiver. The angles θi and θr are varied be-

tween 5˚ and 90˚ degrees, while φi and φr are varied between 0˚ and 360˚. It is noted that

the transmitter range and receiver range need not be considered, because the RCS does not

depend on the ranges from the transmitter and receiver, assuming that the target is in the

far-field of both the transmitter and receiver.

To begin the experiment, the transmitter is placed at a fixed location. The receiver is then

placed at various locations around the boat, in spherical coordinates with 5˚ increments in

θr and φr. The RCS is computed for each receiver position. Therefore, for each fixed

transmitter position, there are 1,314 receiver positions, at each of which an RCS measure-

ment is predicted. The transmitter is positioned at various locations around the boat, with

30˚ increments in θi and φi.

Figure 42: Coordinate system for bistatic RCS modelling.

Table 6 shows the modelled monostatic RCS for a number of transmitter positions. There


is a large variation in RCS as the elevation angle θi varies. The RCS is largest when θi=60˚.

This large RCS value appears to be caused by a strong reflection from the bench seats. In

Annex A, a full analysis of the monostatic and bistatic RCS is presented. It is shown that 0

dBsm is the median monostatic RCS of the Legend small boat. This value for RCS will be

used in the following sections. Although bistatic radar is not considered in this report, the

bistatic RCS analysis would be useful in designing bistatic radar systems in the future.

Table 6: Monostatic RCS of the Legend small boat, for transmitter locations specified by

θi and φi.

θi (˚) φi (˚) RCS (dBsm)

10 0 5.95

30 0 10.65

60 0 33.17

90 0 -34.87

10 30 0.39

30 30 10.80

60 30 18.85

90 30 -48.64


8 Assessment of Small Boat Detection

Performance

In this section, monostatic UAV radar performance is assessed using simple models for

signal, noise, and clutter power. Performance is computed as a function of various radar

parameters, which allows for quantification of the tradeoffs in parameter selection. Table 7

gives the radar system parameters used in this study.

Table 7: Radar system parameters for small boat detection.

Parameter Definition Value

P peak power (W) 1,500

τ pulse width (µsec) 50

GT transmitter antenna gain

σ target RCS (m2) 1

Ae effective area of receiver antenna (m2)

Rg radar-to-target ground range (km)

h radar altitude (km) 1

k Boltzmann’s constant (Ws/˚K) 1.38×10−23

T0 noise temperature (˚K) 290

Fn noise figure (dB) 4.3

Ls system losses (dB) 5.0

NP number of pulses 128

σ0 clutter cross-section per unit area (m2/m2)

AC area of clutter cell (m2)

PRF pulse repetition frequency (kHz) 4

For monostatic radar the SNR with coherent integration is given by

S

N= NP

PτGT σAe

(4π)2R4kT0BFnLs. (9)

The received power from a clutter patch with RCS σC is given by

SC =PτGT σCAe

(4π)2R4, (10)

The RCS of the clutter patch can be expressed as σC = σ0AC, where σ0 is the radar reflec-

tivity per unit area and AC is the area of the clutter patch. The signal-to-clutter ratio (SCR)

is given by

SCR =√

NPσ

σ0AC

. (11)


The formula for clutter area depends on the relationship of the pulse width to the antenna

footprint. For a grazing angle γ and a beamwidth value β of less than 10˚, the clutter patch

area is given by (see, for example [18])

AC =

{

R(

cτ2

)

βsecγ, if tanγ < Rβcτ/2

,πR2

4 β2 cscγ, otherwise,(12)

where R is the slant range. The first condition corresponds to the pulse width limited case,

while the second condition corresponds to the beamwidth limited case.

The analytical expressions (9) and (11) make it possible to quantify the tradeoffs between

various radar parameters. The Signal-to-Interference Ratio (SIR) will be computed for

various parameter values. When noise is the only interference, the SIR is the SNR. When

noise and clutter are included as interference, the SIR is the Signal-to-Clutter-plus-Noise

Ratio (SNCR). In the following analysis, the SNR and SNCR are plotted as a function of

various parameters. For sea state 2 a wave height of 0.4 m is assumed, while for sea state 5

a wave height of 2 m is assumed. It is noted that for a target return in white, Gaussian noise,

a SNR of 13 dB is sufficient for a Pd of 0.9 with a Pf a of 10−6. When the interference is

due to sea clutter, this relationship does not hold, because sea clutter is neither white nor

Gaussian [19]. However, the 13 dB threshold is plotted as a dashed line in the following

analysis, in order to determine whether the SNR is sufficient to allow for the detection of

small boat threats. The threshold does not apply to the SNCR curves. The baseline radar

parameters are those shown in Table 7, unless stated otherwise. The small boat RCS is

assumed to be 1 m2.

In Figure 43, the SNR and the SNCR are plotted as a function of antenna width, assuming

a square planar antenna. It is seen that the SNR is greater than the 13 dB threshold for all

values of antenna width greater than 0.1 m. The SNCR values for both Sea States 2 and 5

are less than 13 dB. An increase in Sea State from 2 to 5 results in a decrease in the SNCR

of approximately 6 dB. It is noted that the effectiveness of clutter cancellation techniques

depends on both the ratio of signal to interference power and the statistics of the resulting

interference. For example, if the antenna width is 0.5 m and the Sea State is 2, then the

SNCR is 25 dB below the 13 dB threshold for detection. However, clutter cancellation

must increase the SNCR by 25 dB as well as whiten the interference, so that the resulting

interference is white and Gaussian. If this whitening does not occur, then the SNCR may

have to be increased by a larger amount in order to provide detection with Pd of 0.9 and

Pf a of 10−6.

Figure 44 shows the SNR and the SNCR plotted as a function of ground range. The SNR

is greater than 13 dB for all values of ground range. For both Sea States 2 and 5, the SNCR

decreases by 15 dB as the ground range increases from 5 km to 15 km. It is noted that

small boat detection is of particular interest at a range of 5 to 10 km. In Figure 45, the

SNR and the SNCR are plotted as a function of transmitted peak power. The ratio of the


signal power to the clutter power is independent of the peak power, so that the variation of

the SNCR with peak power is negligible. Figure 46 shows the SNR and the SNCR as a

function of the average wave height hav, as defined in the GIT model. Increasing hav from

0.1 m to 4 m results in decreasing the SNCR by 12 dB.

The analysis derives the radar parameters that must be used to achieve sufficient SNR.

Assuming the use of Doppler processing, clutter cancellation will need to be carried out to

detect slow-moving targets in clutter.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1−40

−30

−20

−10

0

10

20

30

40

50

60

Antenna width (m)

Sig

nal−

to−

inte

rfere

nce R

atio (

dB

)

Noise only

Noise + Clutter (Sea State 2)


Figure 43: SIR versus antenna width for a square antenna, P = 1.5kW , Rg = 10km.


5 10 15−30

−20

−10

0

10

20

30

40

50

60

Ground Range (km)

Sig

nal−

to−

inte

rfere

nce R

atio (

dB

)

Noise only



Figure 44: SIR versus ground range for P = 1.5kW and a 0.5 m by 0.5 m antenna.

500 1000 1500 2000−20

−10

0

10

20

30

40

50

Peak Power (W)

Sig

nal−

to−

inte

rfere

nce R

atio (

dB

)

Noise only



Figure 45: SIR versus peak power for Rg = 10km and a 0.5 m by 0.5 m antenna.


0 0.5 1 1.5 2 2.5 3 3.5 4−30

−20

−10

0

10

20

30

40

50

Average wave height (m)

Sig

nal−

to−

inte

rfere

nce R

atio (

dB

)

Noise only

Noise + Clutter

Figure 46: SIR versus wave height for P = 1.5kW , Rg = 10km and a 0.5 m by 0.5 m

antenna.


9 Simulation of UAV Detection of Small

Boats

A monostatic UAV radar for small boat detection was simulated in RLSTAP. The goal of

this simulation was to generate datacubes to evaluate the performance of STAP processing.

The modelled antenna was a 0.4 m-by-0.4 m side-looking, rectangular array with 3 sub-

apertures on receive and 0.1 m spacing between sub-apertures. The transmitted waveform

had a PRF of 4 kW with a pulse width of 40 µs. Coherent pulse intervals of 32 pulses were

transmitted, and the peak transmitted power was 1.5 kW. The UAV is assumed to be flying

above the ship. Simulations were run for a small boat RCS of 1 m2, which is the median

value for monostatic small boat RCS from Figure A.5. The boat is travelling towards

the ship. The littoral clutter background consisted of both land clutter and sea clutter in

sea state 3. Two different target ranges were considered, 5 km and 10 km. In addition,

two different values for the radar azimuth angle were considered, 0˚ and 45˚. Thus four

different simulation scenarios were generated, as specified in Table 8. The range-Doppler

map for Scenario 1 is shown in Figure 47.

Table 8: Simulation scenarios for UAV radar detection of small boats.

Scenario Target Range (km) Azimuth angle (˚)

1 5 0

2 10 0

3 5 45

4 10 45

Figures 48-51 show the improvement factors for the four scenarios considered. Table 9

presents the minimum detectable velocity for each of the four scenarios. As shown in Fig-

ure 48, under Scenario 1 Factored STAP has higher IF values at lower frequencies than

Adapt-then-filter. Both STAP techniques have IFs that are approximately 10 dB greater

than that of Doppler processing, except in the clutter notch. In Scenario 2, the IF of Fac-

tored STAP is slightly higher than that of Adapt-then-filter at lower Doppler frequencies,

as shown in Figure 49. In this case, the performance of the STAP methods is 20 dB better

than that of Doppler processing. Figure 50 shows the IFs for Scenario 3. Once again, Fac-

tored STAP has better performance than Adapt-then-filter. Finally, in Scenario 4, as shown

in Figure 51, the IFs are seen to be similar to those from Scenarios 2 and 3.

Table 9 shows minimum detectable velocity and usable Doppler space fraction for all four

scenarios. In all scenarios, Doppler processing has the largest MDV and smallest UDSF,

while Factored STAP has the smallest MDV and largest UDSF. The use of STAP provides

a significant improvement in MDV. In Scenario 1, the improvement is 0.9 m/s, and in

Scenario 4, the improvement is 2.8 m/s.


Table 9: Minimum detectable velocity (MDV) and usable Doppler space fraction (UDSF)

for small boat detection scenarios.

Scenario 1 Scenario 2 Scenario 3 Scenario 4

Doppler MDV (m/s) 4.7 5.6 5.6 6.6

processing UDSF (%) 84.4 81.3 81.3 78.1

Adapt-then-filter MDV (m/s) 3.8 3.8 4.7 4.7

UDSF (%) 87.5 87.5 84.4 84.4

Factored MDV (m/s) 2.8 2.8 2.8 3.8

STAP UDSF (%) 90.6 90.6 90.6 87.5

Doppler (Hz)

Ra

ng

e (

km

)

−1000 −500 0 500 10002.5

5

7.5

10

12.5

15

17.5

20

−14

−12

−10

−8

−6

−4

Target

Figure 47: Range-Doppler map of returns from Scenario 1.


−2000 −1500 −1000 −500 0 500 1000 1500 2000−10

0

10

20

30

40

50

60


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP

Figure 48: STAP improvement factors for Scenario 1.

−2000 −1500 −1000 −500 0 500 1000 1500 2000−10

0

10

20

30

40

50

60


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP



−2000 −1500 −1000 −500 0 500 1000 1500 20000

10

20

30

40

50

60


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP


−2000 −1500 −1000 −500 0 500 1000 1500 20000

10

20

30

40

50

60

70


Impro

vem

ent F

acto

r (d

B)

Doppler processing


Factored STAP



10 Payload Considerations

In this report, radar systems have been proposed to carry out the over-the-horizon detec-

tion of cruise missiles and the detection of small boat threats. This section considers the

UAV payloads that are required to implement such systems. A full design of the proposed

radar systems is beyond the scope of this report. However, an estimate of the size and

weight of the payloads are provided, based on the payload of an operational UAV radar.

Consideration is also given to the UAV platforms that would host the payloads described.

10.1 Bistatic radar for cruise missile detection

A bistatic radar was proposed for the over-the-horizon detection of cruise missiles. The

transmit platform emits pulsed waveforms with 1.5 kW peak power, with a pulse width of

3.9 µs. The transmit antenna is 0.4 m by 0.4 m planar array. The receive platform has 1 m

by 3 m antenna and hosts the receiver, including the synchronizer and processor.

To estimate the size and weight of the transmitter and receiver payloads, the payload of the

Raytheon SeaVue surveillance radar is used as a starting point. The SeaVue is an X-band

SAR/MTI radar that is deployed on helicopters and fixed-wing aircraft and is operated by

the U.S. and other countries around the world. Table 10 summarizes the SeaVue radar

payload.

Table 10: Payload description for the Raytheon SeaVue radar.

Component Size Weight (kg)

Transmit amplifier (15 kW peak) volume = 44.4 dm3 30

Receiver-exciter-synchronizer-processor (RESP) volume = 50.0 dm3 37

Antenna 1.24 m by 0.65 m 23

Total Payload 90

In a bistatic radar system, the transmit platform does not require receiver or processor.

Similarly, the receive platform does not include a transmit amplifier. It is assumed that the

components of the bistatic radar system are similar to those of the SeaVue system. As a

result, the parameters from Table 10 can be used to estimate the payload of the bistatic

system. The bistatic radar antennas are assumed to have a density of 0.74 kg/dm3, which

equal to that of SeaVue. The bistatic system transmitter has a peak power of 1.5 kW, and

it is assumed that the amplifier has a weight of 25 kg, compared to 30 kg for the 15 kW

SeaVue amplifier. An estimate of the bistatic system payloads are summarized in Table 11.

Based on the receiver payload weight of 123 kg, the receive platform would likely be a

medium altitude long endurance (MALE) UAV or a larger tactical UAV. Due to the size of


Table 11: Payload estimate for the bistatic radar for cruise missile detection.

Platform Component Size Weight (kg)

Transmitter Transmit amplifier (1.5 kW peak) volume = 44.4 dm3 25

Exciter-synchronizer volume = 50.0 dm3 12


Total Payload 42

Receiver RESP volume = 50.0 dm3 37

Antenna 1 m by 3 m 86

Total Payload 123

the receive antenna, a MALE UAV platform would be required to host the antenna. The

weight of the UAV platform would need to be large enough so that a side-mounted antenna

does not destabalize the platform in-flight. Furthermore, the 3 m length of the antenna

necessitates the use of a longer airframe.

The transmit platform would likely be a tactical UAV. The payload weight and the relatively

modest size of the antenna allow flexibility is choosing a platform. The UAV control ground

stations for both platforms would need to be located on the ship. This would allow for UAV

radar detections at the receiver to be used to cue the shipborne radar. The ship-based ground

station for the transmitter would also be used to deploy the transmitter in the direction of

the expected threat.

Current UAV radars are monostatic, and as a result, the design of the UAV radar system

would need to take bistatic operation into account. In particular, timing and synchroniza-

tion between the transmitter and receiver would need to be carefully considered, in order

to benefit from the advantages of coherent signal processing.

10.2 Monostatic radar for small boat detection

For small boat detection, a monostatic radar was proposed with a peak power of 1.5 kW

and a pulse width of 50 µs. The antenna is a 0.4 m by 0.4 m planar array. Once again, the

Raytheon SeaVue radar is used as a starting point for estimating the radar payload. The

estimates are presented in Table 12. The estimated payload weight of 67 kg and the modest

antenna size suggest that the radar platform would likely be a tactical UAV.


Table 12: Payload estimate for the monostatic radar for small boat detection.

Component Size Weight (kg)

Transmit amplifier (1.5 kW peak) volume = 44.4 dm3 25

RESP volume = 50.0 dm3 37


Total Payload 67


11 Conclusions

This report evaluated the detection performance of UAV radar systems for two distinct

applications, the over-horizon detection of cruise missiles and the detection of small boat

threats. In both cases, the goal was to develop a UAV radar that would meet the required

detection performance while reducing the size and power of the UAV payload.

For the cruise missile detection problem, a cruise missile with an RCS of 0.1 m2 and a

velocity of Mach 0.75 was assumed. The goal of the radar system was to detect the threat

when it passed through a rectangular barrier. Against this threat, a previous analysis had

proposed a bistatic radar with a receiver located above the ship and the transmitter deployed

in the direction of the expected threat. In this study, the performance of this bistatic system

was analyzed using simulation in RLSTAP. The use of high-PRF waveform allowed for the

unambiguous Doppler estimate of a cruise missile travelling at speeds up to Mach 2. A

ship self-defence configuration and two area air defence configurations were considered.

For ship self defence, the simulation analysis showed that MDV was 46.3 m/s when STAP

processing was employed. Furthermore, it was shown that the radar failed to detect crossing

targets, which are less of a threat in ship self-defence. For area air defence, the MDV was

also 46.3 m/s with STAP processing. However, targets heading for the ship-of-interest at an

angle may not be detected by the UAV radar, due to the larger bistatic angles associated with

the radar geometry. Therefore, the UAV radar is effective in providing ship self defence

capability, but may be impeded in providing area air defence if the bistatic geometries are

not favourable. It is estimated that transmitter payload has a weight of 42 kg and would

require a tactical UAV as a platform. The receiver payload has a weight of 123 kg and

would require a MALE UAV platform.

For small boat detection, the monostatic and bistatic RCS of the Legend small boat was

analyzed using the software Rapport. It was shown that the median monostatic RCS was

1 m2. A parametric analysis of a monostatic UAV radar was carried out. The resulting radar

was modelled in RLSTAP, using a low PRF waveform. It was seen that Factored STAP

had better performance against the small boat threat, both in terms of higher improvement

factor and lower MDV. For four simulated scenarios, Factored STAP resulted in MDVs

between 2.8 m/s and 3.8 m/s. STAP provides significant benefit in the detection of small

boats by decreasing the MDV. Further work in this area would involve verifying UAV radar

performance using experimental data. For small boat detection, the radar payload has an

estimated weight of 67 kg and would require a tactical UAV as a platform.




Annex A: Bistatic Small Boat RCS

This annex presents an analysis of the bistatic RCS of the Legend small boat. This analysis

could be used in the future to aid in the design of bistatic UAV radar systems for small boat

detection.

Figures A.1 to A.4 show Mercator plots of the bistatic RCS of the Legend boat for various

transmitter positions. In all cases, φi=0˚. As was the case for the monostatic RCS, the

bistatic RCS varies greatly with elevation angle. In Figure A.1, θi=10˚, and the bistatic

RCS has a limited number of regions with large values. The most prominent occurs when

the receiver is located near θr = 10˚ and φr = 180˚. In Figure A.2, θi = 30˚. In this case,

there is a larger region where the RCS is large, although the RCS may vary significantly for

small changes in transmitter location. When θi = 60˚, as shown in Figure A.3, the bistatic

RCS is greater than 25 dB for almost all receiver locations between θr = 40˚ and 80˚. In

Figure A.4, θi = 90˚, and the bistatic RCS is largest for receiver locations between θr = 50˚

and 80˚, although the RCS values are smaller than those in Figure A.3.

φr (degrees)

θr (

degre

es)

0 50 100 150 200 250 300 350

0

10

20

30

40

50

60

70

80

90

RC

S (

dB

sm

)

−10

−5

0

5

10

15

20

25

30

Figure A.1: Bistatic RCS in dBsm when the transmitter location is specified by θi=10˚ and

φi=0˚.

Another approach to analyzing the bistatic RCS data is to consider the RCS as a function

of bistatic angle. For all RCS measurements, the bistatic angle is computed for each

combination of the transmitter and receiver positions. For each bistatic angle, the median

and various percentiles of bistatic RCS can then be computed. This approach reduces the

number of variables in the analysis. It also shows if there are particular bistatic angles that


φr (degrees)

θr (

degre

es)

0 50 100 150 200 250 300 350

0

10

20

30

40

50

60

70

80

90

RC

S (

dB

sm

)

−10

−5

0

5

10

15

20

25

30


φi=0˚.

φr (degrees)

θr (

degre

es)

0 50 100 150 200 250 300 350

0

10

20

30

40

50

60

70

80

90

RC

S (

dB

sm

)

−10

−5

0

5

10

15

20

25

30


φi=0˚.


φr (degrees)

θr (

degre

es)

0 50 100 150 200 250 300 350

0

10

20

30

40

50

60

70

80

90

RC

S (

dB

sm

)

−10

−5

0

5

10

15

20

25

30


φi=0˚.

result in especially low or high values of RCS. Bistatic angles are grouped into bins with 3˚

bin widths, with the exception of the monostatic case (bistatic angle = 0˚) which is grouped

into a distinct bin.

Figure A.5 is a plot of the bistatic RCS statistics. For very small bistatic angles near 0˚ and

very large bistatic angles near 180˚, the bin counts are small and the resulting configurations

are not a regular sampling of the bistatic angles in an “angle” bin. As a result, the bistatic

RCS statistics for these bins may be somewhat “skewed”.

In the monostatic case, the median RCS is 0 dBsm, while the 85th percentile and 15th

percentile occur at 5 dBsm and -5 dBsm. For all bistatic angles, 5 dBsm is a good approxi-

mation for the median RCS. However, the 15th percentile has large fluctuations for bistatic

angles greater than 80˚, reflecting the fact that the RCS has some deep nulls for these large

bistatic angles. In designing a bistatic UAV radar system, it may be beneficial to avoid

geometries that result in a bistatic angle greater than 80˚.


0 20 40 60 80 100 120 140 160 180−50

−40

−30

−20

−10

0

10

20

30

40

50

bistatic angle (degrees)

RC

S (

dB

sm

)

85th percentile

median

15th percentile

Figure A.5: RCS statistics for 10 GHz, VV polarization. Monostatic RCS values are

indicated by asterisks.


References

[1] Thomson, A. and Riseborough, E. (2006), Equipment and Measurements for

Evaluating RF Performance Prediction at the SISWS Environment Modelling Trials,

(DRDC Ottawa TM 2006-149) Defence R&D Canada – Ottawa.

[2] Tsunoda, S., Pace, F., Stence, J., Woodring, M., Hensley, W., Doerry, A., and

Walker, B. (2000), Lynx: A High-Resolution Synthetic Aperture Radar, In

Proceedings of the 2000 IEEE Aerospace Conference, pp. 51–58.

[3] Giret, R., Jeuland, H., and Enert, P. (2004), A Study of a 3D-SAR concept for a

millimeter wave imaging radar onboard an UAV, In Proceedings of First European

Radar Conference, pp. 201–204.

[4] Goktogan, A.H., Booker, G., and Sukkarieh, S. (2006), Field and Service Robotics,

pp. 311–320, Springer Berlin.

[5] Scott, R. (2003), Russia’s anti-ship arsenal targets export markets, Jane’s Navy

International.

[6] Scott, R. (2005), Anti-ship weapons updated to target the shore, Jane’s Navy

International.

[7] Lok, J. Janssen (2005), Modern navy missiles march on, International Defence

Review.

[8] Moo, P.W. (2001), Bistatic RCS Predictions for a Cruise Missile, (DREO TM

2001-167) Defence R&D Canada – Ottawa.

[9] Moo, P.W. (2006), UAV Radar Concepts for Over-the-Horizon Detection of

Low-Altitude Threats, (DRDC Ottawa TM 2006-254) Defence R&D Canada –

Ottawa.

[10] Walker, D. (2000), Experimentally motivated model for low-grazing angle radar

Doppler spectra of the sea surface, IEE Proceedings on Radar, Sonar and

Navigation, 147(3), 114–120.

[11] Walker, D. (2001), Doppler modelling of radar sea clutter, IEE Proceedings on

Radar, Sonar and Navigation, 148(2), 73–80.

[12] Willis, N.J. (1995), Bistatic Radar, Technology Services Corp.

[13] Moo, P.W. (2001), Properties of Bistatic Clutter Ridges with Applications to

Space-Based Radar, (DREO TM 2001-139) Defence R&D Canada – Ottawa.

[14] Hughes, S. (2001), Procedure for creating land cover data for use with RLSTAP,

(DREO TN 2001-207) Defence R&D Canada – Ottawa.


[15] Horst, M., Dyer, F., and Tuley, M. (1978), Radar Sea Clutter Model, In IEE

International Conference on Antennas and Propagation, pp. 6–10.

[16] Brennan, L. and Reed, I. (1973), Theory of adaptive radar, IEEE Trans. On

Aerospace and Elec. Systems, 9, 237–251.

[17] Ward, J. (1994), Space-time adaptive processing for airborne radar, (Technical

Report 1015) MIT Lincoln Laboratory.

[18] Eaves, J.L. and Ready, E.K. (1987), Principals of Modern Radar, New York:

Chapman and Hall.

[19] Ward, K., Baker, C., and Watts, S. (1990), Maritime Surveillance Radar; Part 1:

Radar scattering from the ocean surface, Proceedings of the IEE, 137 (F), 51–62.


DOCUMENT CONTROL DATA(Security classification of title, body of abstract and indexing annotation must be entered when document is classified)

1. ORIGINATOR (The name and address of the organization preparing the

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( ) Defence departments and Canadian defence contractors; further distribution only as approved

( ) Government departments and agencies; further distribution only as approved

( ) Defence departments; further distribution only as approved

( ) Other (please specify):

12. DOCUMENT ANNOUNCEMENT (Any limitation to the bibliographic announcement of this document. This will normally correspond

to the Document Availability (11). However, where further distribution (beyond the audience specified in (11)) is possible, a wider

announcement audience may be selected.)

Full unlimited announcement.

13. ABSTRACT (A brief and factual summary of the document. It may also appear elsewhere in the body of the document itself. It is highly

desirable that the abstract of classified documents be unclassified. Each paragraph of the abstract shall begin with an indication of the

security classification of the information in the paragraph (unless the document itself is unclassified) represented as (S), (C), (R), or (U).

It is not necessary to include here abstracts in both official languages unless the text is bilingual.)

This report analyzes the performance of a bistatic uninhabited aerial vehicle (UAV) radar to detect

cruise missile threats beyond a ship’s horizon. The bistatic system, together with adaptive signal

processing is shown to provide self-defence capabilities against targets that are heading towards

the ship. For area air defence, the UAV radar may not be able to detect some targets, depending

on the bistatic geometry. A monostatic UAV radar for small boat defence is also developed. The

radar cross section (RCS) of a small boat is modelled, and it is shown that the median monostatic

RCS is 1 m2. Performance of the UAV radar is analyzed by simulating the small boat scenario

in RLSTAP. With the use of Factored space-time adaptive processing (STAP) to reduce clutter

power, the resulting minimum detectable velocity is shown to be between 2.8 m/s and 3.8 m/s.

Payload sizes and weights are estimated for the proposed radars.

14. KEYWORDS, DESCRIPTORS or IDENTIFIERS (Technically meaningful terms or short phrases that characterize a document and could

be helpful in cataloguing the document. They should be selected so that no security classification is required. Identifiers, such as

equipment model designation, trade name, military project code name, geographic location may also be included. If possible keywords

should be selected from a published thesaurus. e.g. Thesaurus of Engineering and Scientific Terms (TEST) and that thesaurus identified.

If it is not possible to select indexing terms which are Unclassified, the classification of each should be indicated as with the title.)

Uninhabited aerial vehicles (UAVs)

bistatic radar

cruise missile detection

small boat detection

Documents

Performance of UAV Radar Concepts for Naval Defencecradpdf.drdc-rddc.gc.ca/PDFS/unc94/p531349_a1b.pdf · · 2012-08-03Sommaire Performance of UAV Radar Concepts for Naval Defence