Sparse MIMO synthetic aperture sonar processing with distributed optimization

被引：2

作者：

Xenaki, Angeliki ^{[1
]}

Pailhas, Yan ^{[1
]}

Sabatini, Roberto ^{[1
]}

机构：

[1] NATO, Sci & Technol Org, Ctr Maritime Res & Expt, I-19126 La Spezia, Italy

来源：

2020 54TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2020年

关键词：

MIMO; SAS; sparse reconstruction; distributed optimization;

D O I：

10.1109/IEEECONF51394.2020.9443395

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Synthetic aperture sonar (SAS) provides high-resolution seafloor mapping by processing coherently the backscattered signal recorded over consecutive pings. Multiple-input multiple-output (MIMO) configurations improve the spatial sampling along the synthetic aperture at the expense of reduced image contrast due to the cross-correlation between the transmitted waveforms. In this paper, MIMO SAS imaging is formulated as a sparse signal reconstruction problem and is solved in a distributed way with the alternating direction method of multipliers (ADMM). Sparse reconstruction with ADMM outperforms conventional MIMO SAS imaging in terms of resolution and sidelobe suppression, alleviating the impact of waveform correlation.

引用

页码：82 / 87

页数：6

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