Complex-Valued Sparse SAR-Image-Based Target Detection and Classification

It is known that synthetic aperture radar (SAR) images obtained by typical matched filtering (MF)-based algorithms always suffer from serious noise, sidelobes and clutter. However, the improvement in image quality means complexity of SAR systems will increase, which affects applications images. The introduction sparse signal processing technologies into imaging proposes a new way to solve this problem. Sparse recovery show better performance than complex with lower higher signal-to-noise ratios (SNR). As most widely applied fields images, target detection classification rely on high quality. Therefore, paper, framework based recovered approximate message passing (CAMP) algorithm novel network via reconstructed iterative soft thresholding (BiIST) are proposed. Experimental results have whether for or MF-based algorithms, validates huge application potentials