3 1 3 Optimization of a Seven Element Antenna Array

7/28/2019 3 1 3 Optimization of a Seven Element Antenna Array

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Corporate Communications

April 2007

Optimization of a seven element antenna arrayM. Poian1, S. Poles1, F. Bernasconi1, E. Leroux2, W. Steff3, M. Zolesi3

CST UGM2009 Darmstadt, 16-18 March 2009

1 ESTECO s.r.l, - AREA SCIENCE PARK, Padric iano 99, I-34100 Trieste, Italy

[email protected]; [email protected]; [email protected];

2 CST AG - Via Spartaco 48, I-24043 Caravaggio (BG), Italy

emmanuel . leroux@cst .com;

3 Thales Alenia Space - Via Saccomuro 24 I-00131 Rom e, Italy

[email protected]; marcel [email protected] ;


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All rights reserved2007, Thales Alenia Space


April 2007

The global market of L/S band multi-beam antennas for the MobileSatellite Services (MSS) requires large unfurlable antennas

(typically 12m) which are asked to generate a number of spot beams

that can vary in a large range (from a few units to several hundreds).

The MSS antennas are typically required to operate in circula polarization

and to handle a relatively high power level (typically in the KW range).

Introduction


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April 2007

The horn design is driven by the copolar and crosspolar radiation

patterns and gain. Due to the mutual coupling, the radiation pattern is

not entirely governed by the electric field distributed on the aperture

of the excited feed element and it is also influenced by the contributes

which originate from the adjacent apertures acting as passive radiators.The analyses carried out at antenna level show that this phenomena tend to

produce a degradation of the antenna gain performance. For this reason,

the radiative performances of the embedded feed are better specified by

prescribing a canonical pattern shape to which the actual pattern has to be

conform within a certain tolerance.

Radiating element design


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April 2007

The electrical design/optimization of

the radiating element has beenperformed by means of CST Microwave

Studio. After having identified the

geometrical parameters, which are most

Important for the control of the mutual

coupling, the geometry has beenoptimized for the best fit of radiation

pattern versus the prescribed canonical

shape.

If not compensated mutual coupling

generates:

Ripple in the directivity pattern

Increasing of the cross polar level

D=207mm

(1.047 )

L=200 mm

(~ 1 )

50 coax ports

(to 3dB hybrid)

stacked patches

Radiating element design


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April 2007

The Antenna Model


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7/16All rights reserved2007, Thales Alenia Space


April 2007

Model Parameters

In this study, we considered eight free parameters, all geometric:

a1 and a2 are lengths of the two rectangular patches inside the cavity

K1 and K2 are the ratios of dimensions with respect to x-dimensions, that

is with K1=K2=1 wed have perfectly square patches.

HL1 is the distance between one patch and the cavity

HL2 is the distance between the two patches

WG_B and H3R represent respectively one side of the cavity-base and the

length of a waveguide section between the radiating aperture and the

cavity-base.




April 2007

Optimization Targets

The Return Loss for each of the two ports (two for each of the antenna

elements, in order to obtain circular polarization); the Return Loss is

an indicator of how much the matching is non ideal, so it is to be

minimized.

As a second, parallel target, the electromagnetic coupling between ports

is to be minimized

The cross-polarization component is also to be minimized, as it indicates

the presence of a spurious signal with polarization opposite to thedesign specifications.




April 2007

Looking for optimal:

length of the first rectangular patch inside the cavity: a1 [60, 63]

length of the second rectangular patch inside the cavity: a2 [83, 86]

ratios of dimensions with respect to x-dimensions:

K1 [0.98, 1.02], K2 [1, 1.05]

distance between one patch and the cavity: HL1 [3 , 4.5]

distance between the two patches: HL2 [18, 21]

cavity-base: WG_b [98, 102]

length of a waveguide section: H3r [0.1, 0.3]

Design Space




April 2007

modeFRONTIER4 / CST MWS direct interface




April 2007

The CST direct interface properties




April 2007

CST optimization setup Table Creation




April 2007

Pareto Frontier

In multi-objective optimization, many objective functions are involved.

The best solution is the design with simultaneously the maximum value for

all the objective functions. Such a design does usually not exist.

The concept of best design is replaced with the concept of dominant design.

Better, a set of dominant designs is the typical result of a multi-objectiveoptimization. This set of dominant designs is the so-called Pareto Frontier.

Figure is a qualitative representation of a Pareto Frontier in a two-objective

optimization if the two objective functions f1 and f2 have to be maximized.




April 2007

First optimization: MOGT

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ObjectiveS11

In the first phase Multi-Objective Game Theory is used in order to have afast mapping of the Pareto frontier




April 2007

Second optimization: MOGA

In the second phase a Genetic Algorithm refines the Pareto frontierexploration starting from selected points of the first optimization run

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ObjectiveS11


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April 2007

Conclusions

Once the integration of the model in the optimization environment is done, all

the optimization capabilities can be applied to improve the performance of

the antenna

The optimization is a full batch process (no human intervention during the run

phase), but can be constantly monitored from a run-log graphic console.

Todays multi-core PCs and clusters are able to carry out multi-objectives

optimization of a complex model by a reasonable computational time.

Thanks to its clear, well-defined, and non-weighted approach, multi-objectiveoptimization helps in understanding the physics of the problem while

exploring the design space, looking for the set of optimal configurations.

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3 1 3 Optimization of a Seven Element Antenna Array