基于广义近似消息传递的快速DOA估计方法

doi:10.12305/j.issn.1001-506X.2022.10.01

Abstract

Abstract:

In order to solve the problem of high complexity in direction of arrival (DOA) estimation algorithm of sparse recovery, a method is proposed which employs the generalized approximate message passing (GAMP) based sparse Bayesian learning to estimate DOAs. Based on the existing bi-static passive radar system, this algorithm builds a statistical model of GAMP signals with multiple measurement vectors, simplifies the calculation of high-dimensional joint posterior probability density to a scalar operation, and improves the calculation efficiency of the algorithm. In addition, for off-grid targets, the angle spatial grid updating strategy is derived by the gradient descent method. Simulation results show that the proposed algorithm has higher estimation accuracy and lower computational complexity than other algorithms with a limited number of snapshots and low signal-to-noise ratio.

Key words: direction of arrival (DOA), bi-static passive radar, sparse Bayesian learning, generalized approximate message passing (GAMP)

CLC Number:

TP181

Jun ZHANG, Xinyu ZHANG, Weidong JIANG, Yongxiang LIU, Xiang LI. Fast DOA estimation method using generalized approximate message passing[J]. Systems Engineering and Electronics, 2022, 44(10): 2995-3002.

Figures/Tables 8

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Fast DOA estimation method using generalized approximate message passing

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