High-speed Target ISAR Imaging via Compressed Sensing Based on Sparsity in Fractional Fourier Domain

Compressed sensing (CS) provides great potential to reduce radar sampling rate while improve the imaging performance. In this paper, the application of CS to ISAR imaging of high-speed space targets is introduced. Firstly, based on the analysis of the echo model of highspeed targets, we elucidate that the dechirped high-speed target echo is of sparsity in fractional Fourier domain. Then the Analog-to-information conversion (AIC) is used to take compressive measurements, following which the radar image can be recovered via nonlinear optimization. In particular, considering the non-cooperative characteristic of targets, an optimization search algorithm based on sparsity of the reconstructed range profiles is presented, so as to find the optimal transform order of fractional Fourier transform. Experiment results from both simulated data and measured data show the validity and superiority of the proposed imaging method.