High resolution range profile of compressive sensing radar with low computational complexity

Abstract

On the basis of compressive sensing radar, this study presents a new method of reconstructing the moving target's high-resolution range profile (HRRP) with low computational complexity. With regard to the spatial sparsity of the radar scene, only a few sub-pulses of frequency-stepped chirp signal (FSCS) are used to sample the target's frequency responses. To reduce the computational complexity of traditional compressive sensing (CS) algorithm which uses FSCS to reconstruct moving target's HRRP, a dynamically deduced sensing matrix is established based on target's velocity estimation. Besides, the orthogonality of the deduced sensing matrix is analysed from the perspective of FSCS frequency encoding types to enhance the HRRP construction accuracy using CS algorithms. Numerical simulations demonstrate that the proposed method performs better than traditional algorithm with smaller estimation error and better robustness against noise.