Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques

Abstract

The sparse recovery algorithms formulate synthetic aperture radar (SAR) imaging problem in terms of sparse representation (SR) of a small number of strong scatters’ positions among a much large number of potential scatters’ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning (SBL), fast Bayesian matching pursuit (FBMP), smoothed l0 norm method (SL0), sparse reconstruction by separable approximation (SpaRSA), fast iterative shrinkage-thresholding algorithm (FISTA), and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.