小样本情况下的参数到达方向估计

Manlin Xiao, Xin Qi, P. Wei
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引用次数: 0

摘要

本文研究了阵列处理中的到达方向估计问题。重点是如何在没有大量快照的情况下准确地估计源参数。提出了一种计算复杂度较低的基于幅相估计的参数迭代自适应算法。该算法避免了特征分解和子空间投影,从而减弱了子空间交换现象的负面影响。该估计器适用于有限样本量情况下的DOA估计,尤其适用于解析间隔较近的源。数值评估表明,在观测维数和样本量量级相当的小样本场景下,该估计器比MUSIC算法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parametric direction of arrival estimation in the small sample-size case
The problem of direction of arrival estimation in array processing is considered in this paper. The focus is on how to estimate the source parameters accurately in the absence of a large number of snapshots. A parametric iterative adaptive algorithm based on amplitude and phase estimation with low computational complexities is proposed in this paper. Eigen-decomposition and the subspace projection are avoided in the proposed algorithm, such that the negative influence of subspace swap phenomena is weakened. This estimator is suitable for DOA estimation in the finite sample-size scenario, especially for resolving closely spaced sources. Numerical evaluations indicate that the estimator has a better performance than the MUSIC algorithm in the small sample-size scenario, where the observation dimension and the sample size are comparable in magnitude.
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