Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique

Abstract

A super-resolution reconstruction approach of radar image using an adaptive-threshold singular value decomposition (SVD) technique was presented, and its performance was analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function (PSF) matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar (SAR) images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio (SNR). Five versions of SVD algorithms, namely 1) using all singular values, 2) using the top 80% singular values, 3) using the top 50% singular values, 4) using the top 20% singular values and 5) using singular values s such that s²≥max(s²)/r_inSNR were tested. The experimental results indicate that when the singular value threshold is set as s_max/(r_inSNR)^1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.

Keywords

synthetic-aperture radar / image reconstruction / super-resolution / singular value decomposition / adaptive-threshold

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