Continuity pattern-based sparse Bayesian learning for inverse synthetic aperture radar imaging

Abstract

This paper considers the problem of block-sparse recovery for two-dimensional inverse synthetic aperture radar (ISAR) imaging. According to the scatterer distribution of the target scene in ISAR image, the continuity pattern in both range and cross-range domains should be considered. Therefore, the sparsity of each grid cell is controlled by four neighboring hyperparameters and the relevance between neighboring coefficients is determined by coupling parameters, which are data-dependent, so the estimation is done adaptively by an expectation–maximization algorithm. To model the pattern dependencies among neighboring scatterers on range-Doppler domain, we develop the data-dependent coupling parameters method to capture continuity pattern of ISAR signals. Simulation results show that the proposed method can achieve improvement in terms of entropy, image entropy, and image contrast. Moreover, our algorithm effectively improves reconstruction of target scene in noiseless and noisy case compared with other methods.