DOA Estimation Algorithm for Sparse Representation Under Non-Stationary Noise

doi:10.13190/j.jbupt.2019-049

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (1): 116-121.doi: 10.13190/j.jbupt.2019-049

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DOA Estimation Algorithm for Sparse Representation Under Non-Stationary Noise

WEI Juan¹, CAO Kai-jun¹, NING Fang-li^2,3

1. School of Telecommunications Engineering, Xidian University, Xi'an 710071, China;
2. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
3. Dongguan Sanhang Civil-Military Integration Innovation Institute, Dongguan 523808, China

Received:2019-04-04 Online:2020-02-28 Published:2020-03-27
Supported by:

Abstract

Abstract: In order to improve the direction of arrival (DOA) estimation accuracy of the far-field non-coherent narrow-band signal in non-stationary noise environment, an improved DOA estimation algorithm based on sparse reconstruction is proposed. Firstly, the class differential covariance algorithm is used to construct the difference matrix to suppress the influence of non-stationary noise. Then the sparse representation model and the weight function is constructed based on the basic principle of estimation of signal parameters via rotational invariance technique algorithm. Finally, the DOA estimation is realized by solving the model with weighted l₁ norm. Simulation shows that, compared with the traditional covariance difference algorithm, the noise covariance matrix estimation algorithm, the rank trace minimization algorithm, the sparse reconstruction algorithm, the proposed algorithm can not only suppress the influence of non-stationary noise effectively, but also has strong robustness and high estimation accuracy under low signal noise ratio and low snapshot number.

Key words: non-stationary noise, direction of arrival estimation, sparse reconstruction, weighted l₁ norm

CLC Number:

TN911.7

WEI Juan, CAO Kai-jun, NING Fang-li. DOA Estimation Algorithm for Sparse Representation Under Non-Stationary Noise[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 116-121.

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DOA Estimation Algorithm for Sparse Representation Under Non-Stationary Noise

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