Pattern Synthesis for Sparse Linear Arrays via Atomic Norm Minimization

In this letter, a novel pattern synthesis algorithm that jointly optimizes the number, positions, and excitation of elements in a sparse linear array (SLA) is designed based on continuous compressed sensing. The pattern synthesis problem is first formulated as a sparse recovery problem, which can be...

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Vydáno v:IEEE antennas and wireless propagation letters Ročník 20; číslo 12; s. 2215 - 2219
Hlavní autoři: Wang, Zhen, Sun, Guohao, Tong, Jun, Ji, Yuandong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Atomic norm
Compressed sensing
Convex functions
convex optimization
Domains
Excitation
Finite element analysis
Linear arrays
Matrix decomposition
Minimization
off-grid compressive sensing
Optimization
Semidefinite programming
sparse arrays
Sparse matrices
Synthesis
Toeplitz matrix
Transmission line matrix methods
ISSN:1536-1225, 1548-5757
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Shrnutí:In this letter, a novel pattern synthesis algorithm that jointly optimizes the number, positions, and excitation of elements in a sparse linear array (SLA) is designed based on continuous compressed sensing. The pattern synthesis problem is first formulated as a sparse recovery problem, which can be solved using semidefinite programming involving atomic norm minimization (ANM). The parameters of the designed SLA, including the corresponding element positions and excitations, are determined via Vandermonde decomposition and least squares in the continuous domain. As ANM works directly in the continuous domain, the proposed algorithm is able to synthesize the desired patterns with a small mismatch and a small number of elements. A number of representative numerical experiments show the effectiveness of our algorithm.
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ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2021.3103514