Enhancing GMTI Performance in Non-StationaryClutter Using 3D STAP

Phillip M. Corbell, Jimmie J. Perez, and Muralidhar Rangaswamy

Air Force Research LaboratorySensors Directorate, Electromagnetics Technology Division

Electromagnetic Scattering Branch80 Scott Dr., Hanscom AFB, MA 01731, USA

e-mail: Phillip.Corbell@hanscom.af.mil

Abstract- In side-looking Ground Moving Target Indication in the clutter returns over the range swath of interest [3],(GMTI) radar, the 2-Dimensional (2D) Space Time (azimuth- [4]. This results in elevation-dependent clutter statistics whichDoppler) domain can adequately define a clutter spectrum which has a deleterious impact on classical STAP performance. Thisis accurate for all range gates. However, in applications where thearray boresight is not perpendicular to the velocity vector (e.g. problem ls particularly acute for close-in sensig geometriesforward-looking radar), the azimuth-Doppler clutter spectrum due to the elevation diversity that exists across short sensingexhibits a dependence on elevation angle-of-arrival, creating baselines.range-varying (but elevation-dependent) clutter statistics, or non- Rejecting interference using STAP typically involves "aver-stationary clutter. Classical Space Time Adaptive Processing aging" many presumably target-free range samples to estimate(STAP) algorithms suffer substantial performance losses in non-stationary clutter since classical STAP assumes clutter station- clutter statistics, (i.e. spectral shape and location). The result-arity along the range (training) dimension. Planar arrays are ing interference covariance estimate is then used to synthesizeinherently able to observe the azimuth-Doppler clutter spectrum an adaptive (via the covariance estimate) space-time (2D) filteras a function of the elevation angle, a capability which linear to remove interference from a particular Rangecell Under Testarrays lack. The incorporation of the planar array's vertical (RUT). Thus, having elevation-dependent (i.e., non-stationary)dimension into the joint azimuth-Doppler (2D) STAP domain . '. .in a

has previously resulted in 3D STAP. This paper demonstrates the clutter statistc s in a "blurred" 2D clutter covarianceability of 3D STAP to solve the non-stationary clutter problem estimate which is inaccurate for any one particular rangeby accounting for the elevation-dependent clutter statistics in gate. This poor estimate of the space-time (2D) interferencea 3D covariance matrix. A forward-looking array is used to covariance matrix is correspondingly responsible for poorprovide non-stationary clutter, and the performance of 2D and STAP performance. Therefore, methods that compensate for3D versions of the Adaptive Matched Filter (AMF) and Joint non-stationarity Training Data (TD) are needed to regainDomain Localized (JDL) are used in a close-in sensing paradigm. pormanceeThe results show a >55 dB improvement in output SINR near performancethe clutter null using 3D STAP algorithms in lieu of 2D STAP In the STAP literature, elevation-dependent clutter is of-algorithms applied to the same (subarrayed) data. ten described as heterogeneous clutter [5], range-dependent

clutter [3], [4], or non-stationary clutter [6], [7]. For theI. INTRODUCTION purposes of this paper we use the non-stationary terminology,

Close-in sensing has been suggested for improving surveil- to highlight the geometrical (elevation) dependence of thelance in urban warfare environments where buildings and clutter statistics vs. other types of clutter heterogeneity causedinfrastructure hinder target detection and tracking from long- by non-uniform terrain, urban environments, etc.range sensing platforms. Small, highly maneuverable airborne This paper proposes to address the elevation-dependentplatforms operated in and around these urban "canyons" could clutter statistics using an elevation-aware sensor: the planarbe used to provide all-weather, day-or-night surveillance using array. While planar arrays are sometimes used in the GMTI-short-range Ground Moving Target Indication (GMTI) radars. STAP community, the planar array is often subarrayed into anThese airborne sensing platforms would likely benefit from equivalent linear array prior to adaptive (STAP) processing.a forward-looking array radar, and some have advocated the In recent years, the vertical dimension of the planar array hasuse of Space Time Adaptive Processing (STAP) for providing been incorporated into the STAP algorithm, resulting in 3-interference rejection under such conditions [1], [2]. However, Dimensional (3D) STAP. Research on 3D STAP has mainlyachieving sufficient clutter mitigation with a forward-looking focused on nulling range ambiguous clutter returns in Airbornearray is achallenging taskfroma STAP perspective, especially Moving Target Indication (AMTI) and GMTI applicationswhen employed in a close-in sensing paradigm. in both side-looking [8]-[10] and forward-looking [11], [12]

It is well known that airborne radar arrays oriented any scenarios. Note, the term "3D STAP" has been used elsewheredirection other than orthogonal to the velocity vector cause in the literature with different meanings, and is to be dif-an elevation (or range) dependent angle-Doppler relationship ferentiated from the same term used to describe the addition

1-4244-0284-0/07/$20.00 ©2007 IEEE 647

of "fast-time" Degrees Of Freedom (DOF) to traditional 2DSTAP algorithms for mitigating hot clutter [13], [14]. Thework herein evaluates the ability of 3D STAP techniques 02 2to ameliorate the geometrically-induced non-stationary TD -0,3

-0 4. O4.problem, a concept briefly explored in [2], [15].This paper extends the work of [15] by exploring the

O >h . . - ' - ~~0 5 0 - f i--- -...-D5

tradeoff between the increased TD and computational require-ments due to elevation DOF in 3D STAP vs. the improvedperformance of 3D STAP over 2D STAP in the presence of Fig. 1. Side-Looking Clutter Fig. 2. Forward-Lookingnon-stationary TD. A forward-looking GMTI radar scenario is location in terms of normal- Clutter location in termssimulated to provide the non-stationary TD. The Output SINR ized Doppler (W), Horizontal of normalized Doppler (W),performance of 2D and 3D Joint-Domain Localized (JDL) and Spatial Frequency (t<), and Horizontal Spatial FrequencyAdaptive Matched Filter (AMF) STAP processors operating on Vertical Spatial Frequency (t<), and Vertical Spatialthe same planar array data is compared and analyzed. The 2D (i). Frequency (tz).STAP algorithms are evaluated by first subarray beamformingthe planar array data in the vertical dimension. The optimum2D and 3D STAP Matched Filters are also provided to bound (VSF), which is directly related to the elevation angle-of-performance. arrival. To illustrate, compare the clutter domain [4], [15] for

II. ELEVATION DEPENDENT ANGLE-DOPPLER the side-looking orientation in Fig. 1 with the forward-lookingorientation in Fig. 2, as a function of the joint normalizedCOMPENSATION Doppler (a), normalized HSF (t9 (0, q)), and normalized VSF

The problem of non-stationary clutter occurs in many appli- ( z(0)).cations of interest beyond forwarding-looking STAP, including Figure 1 shows the 3D Q 1 clutter ridge for the side-bistatic/multistatic STAP radar, space-based GMTI (STAP) looking orientation as a function of the normalized Doppler,radar, and maneuvering radar. Many solutions to this problem HSF, and VSF domains. Note that the linear relationship be-have been suggested in the literature [6], [7], [16]-[21]. One tween normalized Doppler and normalized HSF is maintainedsimple, popular approach to mitigating non-stationary clutter is and independent of VSF in the side-looking case.to use reduced DOF STAP algorithms that require less sample Figure 2 illustrates the Q = 1 clutter domain for a forward-support. This has the effect of minimizing the non-stationarity looking array [2], [4]. This clutter "bowl" clearly exhibits aof the TD set, which can result in improved performance [3]. VSF (elevation) dependent clutter "location" in the normalizedAn example of this is illustrated through the 2D JDL results Doppler-HSF (U-t<) plane. Thus, by contrast to the side-in Fig. 7. looking case, the optimal STAP filter for the forward-lookingMost other solutions to the non-stationary TD problem case must exhibit a clutter null which changes both shape and

largely fall into a category known as angle-Doppler compen- location as a function of elevation angle (i.e., range gate). Thesation techniques [16], [18], [21]-[24]. Generally speaking, required shape and location for optimal STAP nulls appear asthese methods transform the TD such that the angle-Doppler the projected contour lines in Fig. 1 and Fig. 2.spectrum (i.e., clutter ridge) of individual TD samples bettermatches the clutter spectrum of the RUT. More will be said IV. SIMULATION MODELS AND PERFORMANCE METRICSabout these techniques in the results section. The 3D clutter models and STAP processors employedA few authors have explored the use of planar arrays and are based on the planar array development in [8] which is

3D STAP to address the problem of non-stationary clutter [1], an extension of the linear array development in [25]. Each[2], [11], [12], [15]. Richardson was perhaps the first author individual range bin is independently modeled, to capture theto suggest using a planar (sub-arrayed circular) array to detect elevation-specific clutter statistics of each range bin. The 2Dslow-moving targets specifically by employing elevation diver- AMF and JDL algorithms are also described in [25].sity [4], and noted that this approach is particularly necessary The performance metric used here is the Output SINR,for detecting targets at steep grazing angles. This paper extends which can be written asand expands this body of research, exploring more processors Ht,72 w 2and explaining how 3D STAP achieves improved performance Output SINR wH (1)over 2D STAP in non-stationary clutter environments.

where (t is the target signal-to-noise ratio, u2 is the noiseIII. ELEVATION DEPENDENT CLUTTER COMPENSATION power, v is the target's space-time steering vector, and R is

USING PLANAR ARRAYS the clairvoyant interference matrix. The output range of thisIn other than side-looking, zero crab angle geometries, the metric is thus dependent on the received target power, which is

training data non-stationarity is due to the changing relation- set equal to an arbitrary value of 1 at the target range (receivingship of the clutter Doppler (fd) and Horizontal Spatial Fre- mainbeam gain) of6 kin,and isvaried over range as afunctionquency (HSF) as a function of the Vertical Spatial Frequency of the illumination pattern. The maximum achievable output

648

TABLE I 60SIMULATION PARAMETERS. 2

Variable 2D Scenario 3D Scenario 5P (Vert. channels)1 (8 el. subarray) 8~~~~~~~~~~~~~~~~~~~~~~~.....N (Horiz. channels) 8 same 40~~~~~~~~~~~~~~~~~~~~~~~~.........1(pulses in CPI) same~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....f~~~~~ (carrier frequency) 1240 MHz same~~~~~~~~~~~~~~~~~~~~~.....

f~~~~~ (pulse repetition freq.) 1984 Hz same ~~~~~~~~~~~~~~~~~~....... .30...(pulsewidth)0.4 ps same~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~........ ........ ........ ........ ........ ........ ...P~~~~~~~~ (transmit power) 1.0 kW same~ ~~ ~~~~~~~~~~~~~~~~~~~~........ ......... ......... .......... ......... ........ ....(bandwidth)2.5 M Hz same 20~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~........ ......... .......... .......... .......... ......... .....ha (aircraft altitude) 3073 sam e~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.......... .................... .......... .......... .......... ......j3 (clutter ridge slope) same~~~~~~~~~~~~~~~~~~~~~~~~~........... HHHHH~c/~~~~~~~~~~~~~tx (transm it az) deg sam 10~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~........... ........... ............ ............ ........... ........... ........

Otx(transmitel)-30.8 deg same~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~............ ............ ............ ............ ............. ........... ........ft (target range) km same~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~............ ............. ............. ............. ............. ............ .........(cl tert gham al) .s 8............ .5..ElementPatternCosinesame Doppler(normalized)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....................................................................Element Gain dB same~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~............. ............................ ............................ ............ ..........Element Backlobe Atten. -90 dB same~~~~~~~~~~~~~~~~~~~~~...

d~~ (vert. elementspacing)c/ (2fo) m same Fig. 3. Output SINR of the optimal 2D or 3D STAP.... Matched.. ...Filter ..(MF)(available range bins) 1000 same rangeof 6km (~~~~~~~~~~~~~~~~~~....... ........ ......... .........0........el.)...

JDL-2 Bins (az el Dopp) 5x1x5 (25 DOF) 5x5x5 (125 DOF)AMF Training Data (TD) 128 1000~~~~~~~~~~~~~~~~~~~~~~~~~~~~~........

th4lterrde indicating... the....... elevation.... dependent...mainbeam.SINR is equal to the maximum noise-only performance, which clutter Doppler, a.k.a. the Doppler of the ground return at each~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.................... .................. ................... ....................................... ................

issynonymouswiththesystemintegrationgain(NMP=8~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~), Figure4 providesthesameinformation..............as.................Fig................. 3,...............but......is....now...in Hthis chasnelaots71) B Ti vauerersaent h pe lte s elevation angle.rathe. than.rang. Contrasting.............Figs......3boundonallOutputSINRresults.TheOutputSINR metricand4highlights importantrealizations relatingtoforward-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.....................................................................................................................is used to call attention to the non-uniform illumination of the looking STAP applied to close-in sensing. large portion..........

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smallregionofelevation.Thiscomparisonalsoillustratesthe~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....................................................fact that the clutter ridge location (and with it the clutter AMF compared to the 2D AMFis...........55.dB.higher..near.the..clutter

statistics) is more aptly described as elevation dependent vs. null (0.4 normalized Doppler) at the target range/elevation..........rangedependent. (6kmI-30.80),illustratinga clear improvementinMinimum~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.........................................Discernable Velocity (MDV)~~~~~~...Not.alo.hat999rage.in

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