Performance Analysis of an Improved MUSIC DoA Estimator
Performance Analysis of an Improved MUSIC DoA Estimator
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DOI：
10.1109/tsp.2015.2465302
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发表时间：
2015-12-01
影响因子：
5.4
通讯作者：
Loubaton, Philippe
中科院分区：
文献类型：
--
作者：
Vallet, Pascal;Mestre, Xavier;Loubaton, Philippe
本文研究了在采样数目和传感器数目都以相同的速率收敛到无穷大的渐近状态下，基于传感器阵列的子空间DOA估计的统计性能。改进的子空间DOA估计器在前人的工作中被推导出来(称为G-MUSIC)，并且在信源数目及其DOA保持不变的情况下被证明是一致的和渐近高斯分布的。在这种情况下，本文证明了传统的MUSIC方法也可以提供与G-MUSIC方法相同的一致的DOA估计，并且具有相同的渐近方差。还考虑了波束宽度量级间隔的DOA的情况，该波束宽度对近间隔的源进行建模。结果表明，G-MUSIC估计仍然能够一致地分离来源，而音乐估计不再是这样。对G-MUSIC估计的渐近方差也进行了估计。
This paper addresses the statistical performance of subspace DoA estimation using a sensor array, in the asymptotic regime where the number of samples and sensors both converge to infinity at the same rate. Improved subspace DoA estimators were derived (termed as G-MUSIC) in previous works, and were shown to be consistent and asymptotically Gaussian distributed in the case where the number of sources and their DoA remain fixed. In this case, which models widely spaced DoA scenarios, it is proved in the present paper that the traditional MUSIC method also provides DoA consistent estimates having the same asymptotic variances as the G-MUSIC estimates. The case of DoA that are spaced of the order of a beamwidth, which models closely spaced sources, is also considered. It is shown that G-MUSIC estimates are still able to consistently separate the sources, while this is no longer the case for the MUSIC ones. The asymptotic variances of G-MUSIC estimates are also evaluated.