Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Bokun Tian,Ling Pu,Xiaoling Zhang,Shunjun Wei,Liang Li,Liming Pu,Jun Shi

doi:10.3390/rs13091751

Abstract

Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

Highlights

Synthetic aperture radar (SAR) is a radar imaging technology and has been applied in different fields such as ocean surface monitoring [1], target identification and classification [2,3], resource exploration [4], and natural calamity monitoring [5] successfully because of its all-day and all-weather working capabilities
We propose a fast Bayesian compressed sensing (FBCS–RVM) algorithm via relevance vector machine to achieve linear array synthetic aperture radar (LASAR) 3D sparse imaging with high efficiency and quality
Since the 3D sparse imaging results are obtained by conducting 2D sparse imaging on every equidistant plane with the fixed preset parameters, we conduct 3D sparse imaging to analyze the stability of the FBCS–RVM algorithm under different imaging scenes with the fixed preset parameters better

Summary

Introduction

Synthetic aperture radar (SAR) is a radar imaging technology and has been applied in different fields such as ocean surface monitoring [1], target identification and classification [2,3], resource exploration [4], and natural calamity monitoring [5] successfully because of its all-day and all-weather working capabilities. Traditional SAR images only reflect the two-dimensional (2D) information of targets while usually lose targets’ information in the height direction; they cannot reflect the three-dimensional (3D) structure of targets. This disadvantage limits the application of SAR seriously, and how to obtain targets’ 3D imaging results is an important research area in SAR imaging fields. Scholars have obtained targets’ 3D information successfully under different SAR modes such as the tomography SAR (TomoSAR) [6], curvilinear SAR (CurSAR) [7], and linear array SAR (LASAR) [8,9].

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Discussion

Conclusion