Measurement Matrix Optimization for ISAR Sparse Imaging Based on Genetic Algorithm

Abstract

Inverse synthetic aperture radar sparse imaging based on compressive sensing has been widely researched. The measurement matrix significantly affects the performance of target imaging. In this letter, focus on the kind of signals that consist of several subpulses with stepped frequency, a measurement matrix optimization method based on genetic algorithm (GA), is proposed. The actual physical observation process is considered and the target characteristics are utilized to optimize the measurement matrix. Then, the expected imaging results can be obtained with minimum data using the optimized measurement matrix. Meanwhile, the orthogonal matching pursuit algorithm is improved for signal reconstruction, which can reduce the computation load significantly. The effectiveness of the proposed method is demonstrated by experiments.