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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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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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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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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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The Antenna Model
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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.
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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.
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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
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modeFRONTIER4 / CST MWS direct interface
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The CST direct interface properties
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CST optimization setup Table Creation
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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.
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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
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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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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.