改进型 MUSIC DoA 估计器的性能分析
Performance analysis of an improved MUSIC DoA estimator
AI总结：
本文分析样本数与传感器数同比增长时改进型 G-MUSIC 的 DoA 估计性能，证明宽间隔源下其与传统 MUSIC 渐近等效，而近间隔源下仅 G-MUSIC 能一致分离。
摘要：
本文研究在样本数与传感器数以相同速率同时趋于无穷的渐近情形下，利用传感器阵列进行子空间 DoA 估计的统计性能。已有工作推导了改进型子空间 DoA 估计器（称为 G-MUSIC），并证明在源数及其 DoA 保持固定时，该估计器具有一致性且渐近服从高斯分布。本文证明，在这一建模宽间隔 DoA 场景的情形下，传统 MUSIC 方法同样能给出一致的 DoA 估计，并且其渐近方差与 G-MUSIC 估计相同。文中还考察了 DoA 间隔约为一个波束宽度的情形，该情形用于建模近间隔源。结果表明，G-MUSIC 估计仍能够一致地分离这些源，而 MUSIC 估计则不再能够做到这一点。本文还评估了 G-MUSIC 估计的渐近方差。
英文摘要：
This paper adresses 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 it is no longer the case for the MUSIC ones. The asymptotic variances of G-MUSIC estimates are also evaluated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
