Robust direction of arrival (DOA) estimation method based on sparse and low-rank recovery

Abstract

The invention belongs to the field of signal processing, and particularly relates to a robust direction of arrival (DOA) estimation method based on sparse and low-rank recovery. According to the technical scheme, firstly, based on a low-rank matrix decomposition method, a received signal covariance matrix is modeled as the sum of a low-rank noise-free covariance matrix and a sparse noise covariance matrix; then the convex optimization problem about a signal and noise covariance matrix is constructed based on a low-rank recovery theory; then a convex model about the sampling covariance matrix estimation error is constructed, and a convex set explicitly includes the convex optimization problem; and finally, based on the obtained covariance matrixes, DOA estimation is achieved through a MVDRmethod. In addition, based on the statistical characteristic that the sampling covariance matrix estimation error submits to progressive normal distribution, an error parameter factor selection criterion is derived to reconstruct the covariance matrixes. Numerical simulation shows that under the limited sampling conditions, compared with traditional CBF and MVDR algorithms, a proposed algorithm ishigh in DOA estimation accuracy and robust in performance.

Description

technical field [0001] The invention belongs to the field of signal processing, and further relates to a robust DOA estimation method based on sparse and low-rank recovery. Background technique [0002] Direction of arrival (DOA) estimation is one of the research hotspots in the field of array signal processing, and has a wide range of applications in the fields of radar, sonar, navigation, wireless communication, speech processing, and radio astronomy. Under noise and interference conditions, many effective DOA estimation methods have been proposed to improve the performance of angle estimation. The conventional beamforming (CBF) algorithm can estimate the target DOA more accurately under the condition of high signal to noise ratio (SNR), however, under the condition of low SNR, its estimation performance will decrease significantly. In response to this problem, Li et al. proposed a minimum variance distortionless response (MVDR) algorithm, which ensures the maximum gain i...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the traditional Direction of Arrival (DOA) estimation algorithm has a large estimation error caused by limited sampling, the present invention proposes a robust DOA estimation method based on low-rank recovery

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