Subarray weighting method for sidelobe suppression of difference pattern based on genetic algorithm

Hang, Hu; Lei, Lili; Hu, Yi; Wu, Qun

doi:10.1109/aps.2008.4619662

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…The array elements are evenly spaced in the

-axis and

-axis directions. Using the method described in reference [ 27 ] to extract the gain pattern of each array element and the coupling matrix

between them, bring the data into Equation (7) for simulation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It is also possible to reduce SLL while maintaining high transmit and receive gains in desired directions. GA is a well-studied method of solving the issue of sidelobe suppression [ 27 ], array partitioning [ 28 ], and sparse arrays [ 29 , 30 ]. Therefore, this paper applies GA to the uniform planar phased array ALSTAR system to design shared aperture transmit and receive subarray under beam constraints.…”

Section: Sparse Shared Aperture For Alstar With Beam Constraintsmentioning

confidence: 99%

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A Sparse Shared Aperture Design for Simultaneous Transmit and Receive Arrays with Beam Constraints

Wei

Xie

et al. 2023

Sensors

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The utilization of efficient digital self-interference cancellation technology enables the simultaneous transmit and receive (STAR) phased array system to meet most application requirements through STAR capabilities. However, the development of application scenario requirements makes array configuration technology for STAR phased arrays increasingly important. Thus, this paper proposes a sparse shared aperture STAR reconfigurable phased array design based on beam constraints which are achieved by a genetic algorithm. Firstly, a design scheme for transmit and receive arrays with symmetrical shared apertures is adopted to improve the aperture efficiency of both transmit and receive arrays. Then, on the basis of shared aperture, sparse array design is introduced to further reduce system complexity and hardware costs. Finally, the shape of the transmit and receive arrays is determined by constraining the side lobe level (SLL), main lobe gain, and beam width. The simulated results indicate that the SLL of the transmit and receive patterns under beam-constrained design have been reduced by 4.1 dBi and 7.1 dBi, respectively. The cost of SLL improvement is a reduction in transmit gain, receive gain, and EII of 1.9 dBi, 2.1 dBi, and 3.9 dB, respectively. When the sparsity ratio is greater than 0.78, the SLL suppression effect is also significant, and the attenuation of EII, transmit, and receive gains do not exceed 3 dB and 2 dB, respectively. Overall, the results demonstrate the effectiveness of a sparse shared aperture design based on beam constraints in producing high gain, low SLL, and low-cost transmit and receive arrays.

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“…The array elements are evenly spaced in the

-axis and

-axis directions. Using the method described in reference [ 27 ] to extract the gain pattern of each array element and the coupling matrix

between them, bring the data into Equation (7) for simulation.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Sparse Shared Aperture For Alstar With Beam Constraintsmentioning

confidence: 99%

A Sparse Shared Aperture Design for Simultaneous Transmit and Receive Arrays with Beam Constraints

Wei

Xie

et al. 2023

Sensors

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show abstract

“…Referring to (10), we can obtain the weight w by the convex optimization algorithm (CVX). It is used to evaluate the contribution of each element in multiple given directions.…”

Section: B Optimize Excitation Valuementioning

confidence: 99%

“…Performance indicators such as sidelobe levels or beam widths are often used as optimization goals [8]. Genetic algorithm [9][10], differential evolution algorithm [11], convex optimization algorithm [12], sequential convex optimization algorithm [13], and particle swarm algorithm [14] are typical examples of heuristic algorithms. Although these algorithms can achieve narrow main lobes and low sidelobes in patterns, they usually require more computing resources than the excitation matching method.…”

Section: Introductionmentioning

confidence: 99%

A Multibeam Subarray Partition Design via Iterative Excitation Amplitude Tournament

Wang,

Li,

Ding

et al. 2023

IEEE Access

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The subarray-level multibeam structure is advantageous for minimizing system complexity and enhancing multi-target detection and estimation performance. Furthermore, the subarray configuration plays a major role in determining the multibeam antenna pattern parameters. Therefore, we propose a subarray partition technique based on the tournament algorithm to enable spatial multibeam synthesis. In this paper, subarray configuration and excitation are alternately optimized through multiple rounds of tournament iterations. Each iteration calculates subarray excitations using convex optimization methods to fulfill requirements for the main radiation direction and low sidelobes, among other performance criteria. At the same time, excitations for each unassigned array element are calculated within each subarray separately. Simulation results for linear arrays demonstrate the proposed algorithm is not only suitable for linear arrays but also applicable to other array configurations, such as circular ring arrays and cylindrical arrays. And the proposed algorithm is effective in subarray periodicity, suppressing sidelobe levels, and allocating resources based on priority.

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“…Recently, a genetic algorithm optimization is used to reduce the sidelobe level for linear array antennas [11,12,18,3,16,4,15]. Genetic algorithm is used for optimizing the array element position for planar array antenna [17,2], the array weighting for difference pattern antenna [8], and the array placement for circular array antenna [9]. This paper optimizes the array elements weights with −30 dB precision in order to reduce the sidelobe level of the radar phased array antenna.…”

Section: Introductionmentioning

confidence: 99%

A sidelobe level reduction (SLL) for planar array antennas with −30 dB attenuators weight precision

Aboul‐Seoud¹,

Mahmoud²,

Hafez³

2010

Aerospace Science and Technology

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Subarray weighting method for sidelobe suppression of difference pattern based on genetic algorithm

Cited by 5 publications

References 3 publications

A Sparse Shared Aperture Design for Simultaneous Transmit and Receive Arrays with Beam Constraints

A Sparse Shared Aperture Design for Simultaneous Transmit and Receive Arrays with Beam Constraints

A Multibeam Subarray Partition Design via Iterative Excitation Amplitude Tournament

A sidelobe level reduction (SLL) for planar array antennas with −30 dB attenuators weight precision

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