A Priori-Based Subarray Selection Algorithm for DOA Estimation

Abstract

A finer direction-of-arrival (DOA) estimation result needs a large and dense array; it may, however, encounter the mutual coupling effect, which degrades the performance of DOA estimation. There is a new approach to mitigating this effect by using a nonuniform array to achieve DOA estimation. In this paper, we consider a priori DOA estimation, which is easily obtained from tracking results. The a priori DOA requires us to pay close attention to the high possibility of where the DOA will appear; then, a weight according to the prior probability distribution of DOA is added to each direction, which leads the sensing matrix of DOA estimation to be near low-rank. Thus, according to the low-rank matrix approximation theory, an optimal low-rank approximate matrix is obtained and an algorithm is proposed to select the elements of the original array according to right singular vectors of the approximate matrix. After that, the impacts of different weights are analyzed, and a mixed weight is presented which has flexibility for common use. Finally, a number of numerical simulations are carried out, and the results verify the effectiveness of the proposed methods.

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

“…MIMO radar has been studied by many scholars because of its advantages in waveform diversity [2][3][4], better resolution, and extended array aperture [5][6][7]. In recent years, direction of arrival (DOA) estimation [8][9][10][11][12][13][14][15] is increasingly linked to MIMO radar. When using conventional subspace class algorithms to estimate the DOA of coherent targets, the vector of the signal subspace penetrates into the noise subspace, resulting in a deficient covariance matrix rank that is unable to accurately estimate the number of coherent signal sources.…”