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

A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition 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）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.

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)/rinSNR were tested.The experimental results indicate that when the singular value threshold is set as smax/(rinSNR)1/2,the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.