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An Off-Grid Block-Sparse Bayesian Method for Direction of Arrival and Polarization Estimation
Circuits, Systems, and Signal Processing ( IF 2.3 ) Pub Date : 2020-02-17 , DOI: 10.1007/s00034-020-01372-3
Pinjiao Zhao , Guobing Hu , Hongcheng Zhou

The problem of DOA and polarization parameter estimation is considered in this paper from a perspective of sparse reconstruction. We present a novel off-grid hierarchical block-sparse Bayesian method for DOA and polarization parameter estimation to improve the estimation accuracy. Firstly, an off-grid model is formulated via the first-order Taylor expansion of the source steering vector. Then, a block-sparse vector is constructed based on sparse Bayesian inference, on which a two-layer hierarchical prior is imposed to promote block sparsity and internal sparsity simultaneously. Finally, the variables and model parameters are updated alternately by adopting the variational Bayesian approximation. In addition, the Cramer–Rao bound for DOA and polarization estimation, the convergence property and the computational complexity analysis of the proposed method are derived. Compared with the existing sparse reconstruction methods and the traditional subspace-based methods, the proposed method can achieve higher estimation accuracy. Simulation results demonstrate the effectiveness and notable performance of the proposed method.

中文翻译:

一种用于到达方向和极化估计的离网块稀疏贝叶斯方法

本文从稀疏重构的角度考虑了DOA和极化参数估计问题。我们提出了一种用于 DOA 和极化参数估计的新型离网分层块稀疏贝叶斯方法,以提高估计精度。首先,通过源导向向量的一阶泰勒展开来制定离网模型。然后,基于稀疏贝叶斯推理构造块稀疏向量,在其上施加两层分层先验以同时促进块稀疏性和内部稀疏性。最后,采用变分贝叶斯近似交替更新变量和模型参数。此外,用于 DOA 和极化估计的 Cramer-Rao 界限,推导出了该方法的收敛性和计算复杂度分析。与现有的稀疏重建方法和传统的基于子空间的方法相比,该方法可以获得更高的估计精度。仿真结果证明了该方法的有效性和显着的性能。
更新日期:2020-02-17
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