论文标题
使用稀疏线性阵列进行DOA估计的稳健且统计上有效的最大可能性方法
A Robust and Statistically Efficient Maximum-Likelihood Method for DOA Estimation Using Sparse Linear Arrays
论文作者
论文摘要
最新的关于到达方向（DOA）估计的研究趋势是通过使用适当的稀疏线性阵列（SLA）和TOEPLITZ协方差结构来局限于传感器，以稳健性对源相关性的构成代价。在本文中，我们尝试使用单个算法同时实现两个目标。为了在统计上有效地定位最大数量的不相关来源，我们提出了一种基于优雅的问题重新纠正和乘数的交替方向方法（ADMM）的随机最大可能性（SML）方法的有效算法。我们证明，尽管在不相关的来源的假设下得出了SML对源相关性的鲁棒性。所提出的算法可用于任意SLA（例如，最小冗余阵列，嵌套阵列和副阵列），并通过{\ em s} equality equality equaltial {em em a} dmm（mesa）命名为{\ em m} aximum-likelihiens {\ em e}刺激。提供了广泛的数值结果，可以协作我们的分析，并证明了最新算法中MESA的统计效率和鲁棒性。
A recent trend of research on direction-of-arrival (DOA) estimation is to localize more uncorrelated sources than sensors by using a proper sparse linear array (SLA) and the Toeplitz covariance structure, at a cost of robustness to source correlations. In this paper, we make an attempt to achieve the two goals simultaneously by using a single algorithm. In order to statistically efficiently localize a maximal number of uncorrelated sources, we propose an effective algorithm for the stochastic maximum likelihood (SML) method based on elegant problem reformulations and the alternating direction method of multipliers (ADMM). We prove that the SML is robust to source correlations though it is derived under the assumption of uncorrelated sources. The proposed algorithm is usable for arbitrary SLAs (e.g., minimum redundancy arrays, nested arrays and coprime arrays) and is named as {\em m}aximum-likelihood {\em e}stimation via {\em s}equential {\em A}DMM (MESA). Extensive numerical results are provided that collaborate our analysis and demonstrate the statistical efficiency and robustness of MESA among state-of-the-art algorithms.
