ISAR Imaging Method for High-speed Spinning Targets based on Minimum Image Entropy Criterion and Compressed Sensing

In accordance with the inverse synthetic aperture radar (ISAR) imaging problem of high-speed spinning targets under the phased array radar system, on the basis of analyzing the influence of the speed estimation error and sparse aperture on the echo resampling model, an ISAR imaging method of high-speed spinning targets based on minimum image entropy criterion and compressed sensing is proposed in this paper. Iterative optimization of the optimal sampling interval based on ISAR image entropy helps to eliminate the negative effects caused by the estimated error of spinning speed. Based on the sparsity of the resampled one-dimensional range profile, the measurement matrix is constructed, which is used to reconstruct the target ISAR images by compressed sensing algorithm. The experimental results indicate that the proposed method has strong adaptive compensation ability for different degrees of speed estimation error, and the reconstructed ISAR image maintains high similarity with the ideal result in conditions of different echo sparsity.