New compressed sensing algorithm for ISAR imaging of maneuvering target

The Fourier basis compressed sensing(CS) algorithm was applied in inversed synthetic aperture radar(ISAR) imaging of smoothly moving target successfully. But it ignored the higher order terms of ISAR echo in azimuth when constructing the ISAR echo model, the sparse representation based on Fourier basis for azimuth information of ISAR echo whose target is maneuvering was invalid, which leaded to the lack of information of Doppler frequency in the local range of time domain. As a result, the imaging results of maneuvering target were blurred in azimuth. The time-frequency analysis technology, because of its good time-frequency characteristics in local, was introduced into the analysis of ISAR echo in azimuth: A Gauss window was used to improve the sparse basis which represented the Doppler frequency of ISAR echo data in a selected short time slice while the size of sparse basis matrix remained constant. Then CS technology which includes time-frequency analysis based sparse basis to represent the echo data, Gauss random observation matrix to reduce the sampling rate and Orthogonal Matching Pursuit (OMP) algorithm to solve the coefficients of the sparse representation was used to analyze the azimuth information in that time slice. As a result of CS's advantage in improving resolution, the resolution of image supported by limited data was high at the same time. The target models of uniformly accelerated motion and varying accelerated motion were both designed to simulate the ISAR echo data of maneuvering target. Compared with the existing imaging methods such as Fourier basis CS algorithm, Range Doppler(RD) algorithm and the Range instantaneous Doppler(RID) algorithm based on Gabor transform, the new one achieves significant improvement in terms of imaging results in azimuth. The corresponding imaging results show the effectiveness of the algorithm from both subjective observation results and objective evaluation indicators including Peak Side Lobe Ratio(PSLR) and azimuth resolution.