A Fast ISAR Imaging Method Based on Strategy Weighted CAMP Algorithm

Complex approximate message passing (CAMP) algorithm is a promising compressed sensing (CS) method for inverse synthetic aperture radar (ISAR) imaging with the merits of fast convergence and good reconstruction. To improve the imaging performance and accelerate the convergence, a new strategy weighted CAMP based ISAR imaging method has been proposed. Rather than solving a sequence of weighted
${l}_{{1}}$
minimization problems, the weighted CAMP problem is solved directly by using a new weighted threshold, which not only ensures the convergence of the algorithm but also improves the convergence speed. To accelerate the convergence furtherly, a new point is chosen smartly in the scalar threshold function, which borrows the idea of fast iterative shrinkage-thresholding algorithm (FISTA). As weighting adaptively to the signal and the noise is more appropriate to maintain the target information and suppress the noise, random forests (RF) algorithm is employed to classify the signal and the noise, and strategy weighting is used based on the classification result. The proposed algorithm was applied into complex sinusoids, quasi-real, and real ISAR data. It has got the best imaging result among the algorithms of CAMP class and need less imaging time than CAMP and weighted CAMP (WCAMP). Therefore, the proposed algorithm is an efficient ISAR imaging method with good performance.