Dictionary Optimization for DOA Approximation in a Single Snapshot

Xiaochuan Wu, Weibo Deng, Qiang Yang
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Abstract

In the Direction of Arrival approximation, Compressed Sensing algorithm will fail to identify correct atoms due to high cumulative coherence of the redundant dictionary (manifold matrix). For insufficient number of samples, especially for a single snapshot, less statistical information aggravates such adverse effects. In this paper, we utilize the observed signal to construct a weighted matrix which can reduce the cross cumulative coherence of the redundant dictionary. And then, we develop a constructive algorithm combing with the weighted matrix to estimate Direction of Arrival. The simulation results prove our algorithm can effectively reduce the signal deviation caused by high coherence of dictionary, specifically in the circumstance of closely spatial sources.
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字典优化的DOA近似在一个单一的快照
在到达方向近似中,由于冗余字典(流形矩阵)的高累积相干性,压缩感知算法无法识别正确的原子。对于样本数量不足,特别是单个快照,较少的统计信息会加剧这种不利影响。在本文中,我们利用观测信号构造一个加权矩阵来降低冗余字典的交叉累积相干性。然后,提出了一种结合加权矩阵的构造算法来估计到达方向。仿真结果表明,该算法可以有效地降低字典高相干性引起的信号偏差,特别是在空间源紧密的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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