Sparse Array Synthesis with Two-Stage Progressive BCS and Undirected Graph-Based Spacing Constraint

Sparse arrays are widely used to achieve full array performance with fewer elements to reduce the cost of array and beamforming computation. Sparse array synthesis methods such as Bayesian compressed sensing (BCS) yield small element numbers; however, they are limited by the tradeoff between complex...

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Bibliographic Details
Published in:Circuits, systems, and signal processing Vol. 43; no. 5; pp. 3118 - 3138
Main Authors: Jiang, Shiyao, Jiang, Rongxin, Liu, Xuesong, Zhou, Fan, Chen, Yaowu
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2024
Springer Nature B.V
Subjects:
Accuracy
Arrays
Beamforming
Circuits and Systems
Cost function
Electrical Engineering
Electronics and Microelectronics
Engineering
Global optimization
Instrumentation
Machine learning
Pattern matching
Sidelobe reduction
Sidelobes
Signal,Image and Speech Processing
Synthesis
Minimum-spacing constraint
Sparse array synthesis
Array signal processing
Surrogate convex cost function
Bayesian compressed sensing (BCS)
ISSN:0278-081X, 1531-5878
Online Access:Get full text
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Summary:Sparse arrays are widely used to achieve full array performance with fewer elements to reduce the cost of array and beamforming computation. Sparse array synthesis methods such as Bayesian compressed sensing (BCS) yield small element numbers; however, they are limited by the tradeoff between complexity and accuracy. Herein, a novel sparse array synthesis method with two-stage progressive BCS and an undirected graph-based element spacing constraint is proposed. The two-stage progressive BCS includes fast on-grid sparsification and accurate off-grid global re-estimation. First, the multitask BCS is solved using a relevance vector machine to efficiently select elements from candidate positions. Subsequently, a convex surrogate cost function is applied to the global re-estimation of the element weights to increase the beam pattern matching accuracy of the sparse array. Global optimization can improve the array performance. In addition, to satisfy the spacing constraint, a weighted merging method based on an undirected graph is proposed and inserted between the two stages to merge elements that are too close, which ensures the processability of the array. Simulations and experiments involving a variety of arrays were conducted to confirm the advantages of the method with regard to array sparsity, sidelobe suppression, beam pattern matching accuracy, and array processability. The proposed method achieved accurate and effective sparse array synthesis and outperformed existing methods.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-023-02597-8