Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference
Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference
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DOI：
10.1109/tsp.2012.2222378
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发表时间：
2013-01-01
影响因子：
5.4
通讯作者：
Zhang, Cishen
中科院分区：
文献类型：
--
作者：
Yang, Zai;Xie, Lihua;Zhang, Cishen
波达方向（DOA）估计是信号处理中的一个经典问题，有许多实际应用。由于基于稀疏信号重构方法的发展，其研究近期取得了进展。虽然这些方法相比传统方法显示出优势，但在实际情况中，当真实波达方向不在离散采样网格上时，仍然存在困难。为处理这种离网波达方向估计问题，本文研究了一种离网模型，该模型考虑了离网波达方向的影响，且建模误差更小。从贝叶斯角度基于离网模型开发了一种迭代算法，同时通过假设所有快拍信号具有拉普拉斯先验来利用不同快拍之间的联合稀疏性。新方法适用于单快拍和多快拍情况。数值模拟表明，所提算法在均方估计误差方面提高了精度。该算法即使在非常粗糙的采样网格下也能保持较高的估计精度。
Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficulties in practical situations where true DOAs are not on the discretized sampling grid. To deal with such an off-grid DOA estimation problem, this paper studies an off-grid model that takes into account effects of the off-grid DOAs and has a smaller modeling error. An iterative algorithm is developed based on the off-grid model from a Bayesian perspective while joint sparsity among different snapshots is exploited by assuming a Laplace prior for signals at all snapshots. The new approach applies to both single snapshot and multi-snapshot cases. Numerical simulations show that the proposed algorithm has improved accuracy in terms of mean squared estimation error. The algorithm can maintain high estimation accuracy even under a very coarse sampling grid.