ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

Abstract

A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.

High‐Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm

“…In radar imaging, the targets are generally composed of strong scattering points, which are sparsely distributed in the imaging region. So radar signal processing based on compressive sensing theory has been widely studied [9][10][11][12], such as SAR imaging [13], ISAR imaging [14,15], and MIMO radar imaging [16]. The CS theory is not sensitive to data loss, which can effectively improve the imaging problem of radar imaging system in the case of data loss and improve the signal processing ability.…”