Sparse Aperture ISAR Imaging of Maneuvering Target Based on Fast Nonuniform Block Sparse Bayesian Learning

Abstract

Inverse synthetic aperture radar (ISAR) imaging of maneuvering targets presents a significant challenge. Maneuvering targets exhibit time-varying Doppler shifts along the azimuth, resulting in a defocused ISAR image. The range instantaneous Doppler (RID) algorithm can achieve instantaneous focused ISAR images of maneuvering targets. However, the performance of RID deteriorates under sparse aperture (SA) conditions and low signal-to-noise ratio (SNR). Although the SA problem can be addressed using compressed-sensing-based RID, it is burdened by significant storage and computational demands. In addition, the ISAR image exhibits a block-sparse structure in the spatial domain. To efficiently obtain the focused ISAR image from SA data in a low-SNR environment, we propose a fast nonuniform block sparse Bayesian learning algorithm. The proposed algorithm reconstructs ISAR images frame by frame to reduce storage and computational demands. To exploit structural features, the sparse signal is modeled using a nonuniform block sparse prior. An inverse-free variational Bayesian inference is developed to achieve accurate inference with high efficiency. In light of the continuity inherent in the time–frequency image, a dimension reduction strategy has been proposed to mitigate computational complexity. Experimental results obtained from both simulated and measured data indicate that the proposed algorithm can efficiently generate ISAR images while effectively preserving the block structure of these images under conditions of SA and low SNR.