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An efficient real-valued sparse Bayesian learning for non-circular signal's DOA estimation in the presence of impulsive noise
Digital Signal Processing ( IF 2.9 ) Pub Date : 2020-08-21 , DOI: 10.1016/j.dsp.2020.102838
Jiacheng Zhang , Tianshuang Qiu , Shengyang Luan

Currently, sparse Bayesian learning (SBL) has been introduced to solve direction of arrival (DOA) estimation in different situations. In the line of DOA estimation under impulsive noise, existing SBL-based methods need large computation which will restrict their practicabilities. To address this problem, we propose an efficient method based on a real-valued SBL for non-circular signals in this paper. Firstly, received signal model is transformed into a real-valued form using the characteristic of non-circular signals' structure. Then, a sparse representation of the modified signal model is constructed in the presence of impulsive noise. Finally, SBL is applied to reconstruct the real-valued sparse model and solve the DOAs estimation. A series of simulations are carried out in different conditions to evaluate the proposed method. Simulation results demonstrate that our method shows better performance than existing methods.



中文翻译:

脉冲噪声存在下非圆形信号DOA估计的有效实值稀疏贝叶斯学习

当前,已经引入稀疏贝叶斯学习(SBL)来解决不同情况下的到达方向(DOA)估计。在脉冲噪声下的DOA估计中,现有的基于SBL的方法需要大量计算,这将限制其实用性。为了解决这个问题,本文提出了一种基于实值SBL的非圆形信号有效方法。首先,利用非圆形信号结构的特点,将接收信号模型转化为实值形式。然后,在存在脉冲噪声的情况下构造修改信号模型的稀疏表示。最后,将SBL应用于重建实值稀疏模型并求解DOA估计。在不同条件下进行了一系列仿真,以评估所提出的方法。

更新日期:2020-08-21
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