Joint Estimation Method of DOD and DOA of Bistatic Coprime Array MIMO Radar for Coherent Targets Based on Low-Rank Matrix Reconstruction

Abstract

Based on low-rank matrix reconstruction theory, this paper proposes a joint DOD and DOA estimation method for coherent targets with bistatic coprime array MIMO radar. Unlike the conventional vectorization, the proposed method processed the coprime array with virtual sensor interpolation, which obtained a uniform linear array to generate the covariance matrix. Then, we reconstructed the Toeplitz matrix and established a matrix optimization recovery model according to the kernel norm minimization theory. Finally, the reduced dimension multiple signal classification algorithm was applied to estimate the angle of the coherent targets, with which the automatic pairing of DOD and DOA could be realized. With the same number of physical sensors, the proposed method expanded the array aperture effectively, so that the degree of freedom and angular resolution could be improved significantly for coherent signals. However, the effectiveness of the method was largely limited by the signal-to-noise ratio. The superiority and effectiveness of the method were proved using simulation experiments.

Joint DOA and DOD Estimation Using KR-MUSIC for Overloaded Target in Bistatic MIMO Radars

“…The interval between the antenna components is half-wavelength, and all array elements are assumed to have omnidirectional unit gain. The total root mean square error (TRMSE) of the DOA and DOD estimation of K targets, 5 , was adopted as the performance indicator, and 10 3 Monte Carlo tests were performed with various parameter settings.…”

“…To reduce the computational load, the ESPRIT-root-MUSIC [4] technique used the ESPRIT and root-MUSIC approaches to estimate the DOA and DOD, respectively. Based on low-rank matrix reconstruction theory, a study [5] proposed a method different from the conventional vectorization method that used virtual sensor interpolation to process coprime arrays and obtain a uniform linear array (ULA) for generating covariance matrices. The scanning of bistatic radars can reach higher degrees of freedom compared with conventional radars, and these degrees of freedom can be used to improve the analytic abilities of angle estimation and overcome cluttering; however, if the number of targets to be detected exceeds that of the maximum analyzable targets (i.e., overloading occurs) [6], the estimation performance of the target direction and angle would largely decline, and the target would become undistinguishable.…”

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“…It might be challenging to grasp how the network came to make its predictions because of the model's interpretability. In addition, training neural networks may be time-and resource-intensive, especially for large datasets [82].…”