An Efficient Maximum Likelihood Method for Direction-of-Arrival Estimation via Sparse Bayesian Learning
An Efficient Maximum Likelihood Method for Direction-of-Arrival Estimation via Sparse Bayesian Learning
复制标题
DOI：
10.1109/twc.2012.090312.111912
复制
发表时间：
2012-10-01
影响因子：
10.4
通讯作者：
Zhou, Yi-Yu
中科院分区：
文献类型：
--
作者：
Liu, Zhang-Meng;Huang, Zhi-Tao;Zhou, Yi-Yu
多维搜索过程的计算限制极大地限制了最大似然到达方向估计方法在实际系统中的应用。在本文中，我们提出了一种基于空间过完备阵列输出公式的高效ML DOA估计器。该方法首先在稀疏性约束下，通过稀疏贝叶斯学习（SBL）在预定义的空间离散网格上重建阵列输出，从而获得一个空间功率谱估计，该估计也表明了源的粗略位置。然后引入一种改进的一维搜索方法，根据重构结果逐一估计信号方向。该方法能够同时估计出事件信号数。数值结果表明，该方法在性能上大大优于现有方法，特别是在低信噪比（SNR）、有限快照和空间相邻信号等苛刻场景下。
The computationally prohibitive multi-dimensional searching procedure greatly restricts the application of the maximum likelihood (ML) direction-of-arrival (DOA) estimation method in practical systems. In this paper, we propose an efficient ML DOA estimator based on a spatially overcomplete array output formulation. The new method first reconstructs the array output on a predefined spatial discrete grid under the sparsity constraint via sparse Bayesian learning (SBL), thus obtaining a spatial power spectrum estimate that also indicates the coarse locations of the sources. Then a refined 1-D searching procedure is introduced to estimate the signal directions one by one based on the reconstruction result. The new method is able to estimate the incident signal number simultaneously. Numerical results show that the proposed method surpasses state-of-the-art methods largely in performance, especially in demanding scenarios such as low signal-to-noise ratio (SNR), limited snapshots and spatially adjacent signals.