In radar system, the maneuvering targets undergoing significant angular motion pose a challenge for motion compensation, thus an inverse synthetic aperture radar (ISAR) imaging framework has been proposed to obtain focused ISAR images under sparse aperture conditions. Due to the nonstationary Doppler frequency induced by the targets’ complex motion, the conventional range-Doppler algorithm suffers from performance degradation. Therefore, we propose a suitable coherent processing interval (CPI) extraction method to find out the relatively stable Doppler range. Since the fact that the extracted CPI typically contains too limited pulses to generate highquality ISAR images, a block-sparse signal recovery method based on pattern-coupled sparse Bayesian learning strategy for matrix block is further developed, which introduces the improved pattern-coupled hierarchical Gaussian model as the sparse prior to reconstruct the scattering coefficient. Specifically, the sparsity of each coefficient is represented by the hyperparameters corresponding to its eight adjacent coefficients, rather than solely by its own hyperparameter. The proposed prior model provides flexibility in modeling any block-sparse signals, and facilitates the generation of block structures. The experimental results using simulated data and measured data demonstrate the validity and superiority of the proposed method.

Inverse synthetic aperture radar (ISAR) imaging; complex motion; block-sparse recovering; CPI extraction; pattern-coupled sparse Bayesian learning (PCSBL)