Combination Complex-Valued Bayesian Compressive Sensing Method for Sparsity Constrained Deconvolution Beamforming

doi:10.1109/TIM.2022.3169537

2022-01-01

PRE

DOI：10.1109/TIM.2022.3169537

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摘要

En 中文

Several deconvolution methods have been proposed to reduce the mainlobe width and sidelobe intensity of conventional beamforming results without increasing the array aperture; however, most of them cannot perform well in the face of coherent targets. Therefore, it is discerning to deconvolve the complex-valued beamforming result rather than the beamforming intensity. However, conventional deconvolution methods focus on the beam intensity, and only a few studies have investigated complex-valued deconvolution beamforming (CDB). Considering the sparse property of the targets in practice, the CDB can be converted to a complex-valued inverse compressive sensing problem and put into the Bayesian framework. To solve it efficiently, a complex-valued relevance-vector-machine (RVM) tool is used to build the complex-valued Bayesian compressive sensing (C-BCS) method that has excellent performance in terms of recovery accuracy and operating speed. However, the C-BCS method is not stable for the CDB because the point spread function is a rank deficiency matrix. To overcome this problem, we propose the combination C-BCS method that combines the block-sparse and C-BCS methods to improve the performance of solving the CDB. The simulation results proved that the proposed method performed better than intensity-based deconvolution methods for the beamforming results generated by the coherent targets. A beamforming image of a pool scene captured by a narrowband forward-looking sonar system was adopted to test various deconvolution methods, and the proposed method exhibited superiority in sidelobe suppression.

Keyword:

Array signal processing

Deconvolution

Bayes methods

Integrated circuits

Compressed sensing

Sonar

Narrowband

Array signal processing

Bayesian compressive sensing (BCS)

coherent targets

complex-valued deconvolution beamforming (CDB)