Sparse Aperture ISAR Imaging and Cross-Range Scaling of Maneuvering Targets Based on Sparse CICPF Method

Inverse synthetic aperture radar (ISAR) plays an irreplaceable role in remote sensing, which takes advantage of strong penetration and high resolution. However, most of the existing ISAR imaging algorithms are based on the assumption that the noncooperative target takes stationary motion and the observed data with full aperture (FA). Unfortunately, in practice, the above assumptions are often no longer held, which brings severe challenges to the traditional ISAR imaging algorithms represented by range-Doppler (RD). In this article, a novel ISAR imaging method of maneuvering targets with sparse aperture (SA) based on sparse coherently integrated cubic phase function (SCICPF) is proposed. The algorithm utilizes the sparsity of linear frequency modulation (LFM) signal in the centroid frequency-chirp rate (CFCR) domain to convert the ISAR imaging into the problem of sparse signal recovery (SSR) in the CFCR domain to avoid the phase error compensation, and the band-exclude local optimization sparsity adaptive matching pursuit (BELO-SAMP) algorithm is proposed to solve the SSR problem. Finally, simulations and real data experiments are performed to validate the superior performance of the proposed algorithm compared with existing algorithms in low signal-to-noise ratio (SNR) and large SA cases.