2-D Joint High-Resolution ISAR Imaging With Random Missing Observations via Cyclic Displacement Decomposition-Based Efficient SBL

In this article, the efficient sparse Bayesian learning (SBL)-based 2-D joint high-resolution inverse synthetic aperture radar (ISAR) imaging approach with random missing observations in both the range-frequency and slow-time domains caused by frequency agility and pulse repetition interval (PRI) jitter is proposed. First, the target return signal model containing the translational motion caused envelope migration and phase error and rotation caused range spatial-variant phase error (RSVPE) with 2-D randomly missing observations is established. Next, considering the rotation caused RSVPE needs to be estimated and compensated for each range cell individually in the range domain, the modified conditional mean estimator-based missing observations recover method and its fast implementation method based on fast Fourier transform (FFT) is designed. Following, the SBL-based cyclic iteration approach is proposed to recover missing observations, estimate target motion parameters and compensate for the translational motion and RSVPE, and achieve the focused and cross-range scaled ISAR image. In addition, to alleviate the computational complexity increase caused by data filling for SBL, a novel low cyclic displacement rank decomposition (LCDRD) for the Toeplitz-block-Toeplitz (TBT) structured covariance matrix of the completed observations in SBL is proposed and applied to design efficient SBL-based ISAR imaging algorithms for two different types of observations. Finally, the effectiveness of the proposed algorithms is validated using both simulated and measured data.