Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources
Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources
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DOI：
10.3390/math10060923
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发表时间：
2022-03
期刊：
影响因子：
2.4
通讯作者：
Lin Jian;Xianpeng Wang;Jinmei Shi;Xiang Lan
中科院分区：
文献类型：
--
作者：
Lin Jian;Xianpeng Wang;Jinmei Shi;Xiang Lan
本文提出了一种基于稀疏贝叶斯学习（SBL）的鲁棒DOA估计方案，用于脉冲噪声和互耦（MC）中的非循环信号。首先利用MC矩阵的Toeplitz性质消除阵列MC的影响，并利用非圆信号的性质扩展阵列孔径。为了消除脉冲噪声的影响，将脉冲噪声的离群部分与信号矩阵中的原始信号一起重建，并利用交替SBL更新算法平衡参数估计的精度和效率来获得DOA粗估计。最后，在搜索到的谱峰附近进行一维搜索，实现高精度的 DOA 估计。通过大量的仿真证明了处理上述错误的算法的有效性和鲁棒性。
In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estimation using the alternating SBL update algorithm. Finally, a one-dimensional search is used in the vicinity of the searched spectral peaks to achieve a high-precision DOA estimation. The effectiveness and robustness of the algorithm for dealing with the above errors are demonstrated by extensive simulations.