A 1-bit compressive sensing approach for SAR imaging based on approximated observation

Chongbin Zhou; Falin Liu; Bo Li; Jingqiu Hu; Yuanhao Lv

doi:10.1117/12.2244975

29 August 2016 A 1-bit compressive sensing approach for SAR imaging based on approximated observation

Chongbin Zhou, Falin Liu, Bo Li, Jingqiu Hu, Yuanhao Lv

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100333J (2016) https://doi.org/10.1117/12.2244975
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Compressive sensing (CS) theory has achieved significant success in the field of synthetic aperture radar (SAR) imaging. Recent studies have shown that SAR imaging for sparse scene can also be successfully performed with 1-bit quantized data. Existing reconstruction algorithms always involve large matrix-vector multiplications which make them much more time and memory consuming than traditional matched filtering (MF) -based focusing methods because the latter can be effectively implemented by FFT. In this paper, a novel CS approach named BCS-AO for SAR imaging with 1-bit quantized data is proposed. It adopts the approximated SAR observation model deduced from the inverse of MFbased methods and is solved by an iterative thresholding algorithm. The BCS-AO can handle large-scaled data because it uses MF-based fast solver and its inverse to approximate the large matrix-vector multiplications. Both the simulated and real data are processed to test the performance of the novel algorithm. The results demonstrate that BCS-AO can perform sparse SAR imaging effectively with 1-bit quantized data for large scale applications.

Citation Download Citation

Chongbin Zhou, Falin Liu, Bo Li, Jingqiu Hu, and Yuanhao Lv "A 1-bit compressive sensing approach for SAR imaging based on approximated observation", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100333J (29 August 2016); https://doi.org/10.1117/12.2244975

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