Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

Abstract

In this paper, a robust angle estimator for uncorrelated targets that employs a compressed sense (CS) scheme following a fast greedy (FG) computation is proposed to achieve improved computational efficiency and performance for the bistatic MIMO radar with unknown gain-phase errors. The algorithm initially avoids the wholly computation of the received signal by compiling a lower approximation through a greedy Nyström approach. Then, the approximated signal is transformed into a sparse signal representation where the sparsity of the target is exploited in the spatial domain. Finally, a CS method, Simultaneous Orthogonal Matching Pursuit with an inherent gradient descent method, is utilized to reconstruct the signal and estimate the angles and the unknown gain-phase errors. The proposed algorithm, aside achieving closed-form resolution for automatically paired angle estimation, offers attractive computational competitiveness, specifically in large array scenarios. Additionally, the analyses of the computational complexity and the Cramér–Rao bounds for angle estimation are derived theoretically. Numerical experiments demonstrate the improvement and effectiveness of the proposed method against existing methods.

Online Gain-Phase Self-Calibration Method of MIMO Array Based on Statistical Characteristics of Target Angle Distribution

“…Li et al [21] proposed a calibration method for coprime MIMO radar, this method estimates the gain-phase errors by trilinear decomposition, iteratively updated based on least squares, and has an ideal performance. Hu et al [22] proposed an efficient compressed sensing based DOA method for bistatic MIMO radar with unknown gain-phase errors, which has a high computational efficiency and stability.…”

“…With the development of machine learning, adaptive technology has made great progress in communication [22][23][24] and recognition [25,26]. Real-time parameter optimization can be realized online in a large number of scenarios according to the environment.…”