Sparse Aperture High-Resolution RID ISAR Imaging of Maneuvering Target Based on Parametric Efficient Sparse Bayesian Learning

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets (MTs) in sparse aperture (SA) conditions is a challenging problem. Range instantaneous Doppler (RID) is useful for ISAR imaging of MT through time-frequency analysis (TFA). However, the performance of RID deteriorates in SA, and frequency resolution is limited by the assumption of a stationary signal in the time window. To tackle these issues, a complex value parametric efficient sparse Bayesian learning (CPESBL) ISAR imaging algorithm is proposed in this letter. In our algorithm, the one-frame signal of MT ISAR imaging is modeled as the multicomponent Chirp signal. This model is solved by CPESBL which contains the complex value efficient SBL (CESBL) with low computational complexity and the Quasi-Newton method estimating the Chirp rate parameter. Then, the focused ISAR image can be obtained efficiently. Moreover, a dimension shrinkage strategy is also proposed to further improve the computational efficiency considering the continuity of sequential ISAR images. With a low computational complexity, the proposed algorithm achieved the best image quality index both in simulated and measured data experiments.