Direction of Arrival Identification Using MUSIC Method and NLMS Beamforming

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI:10.1109/iSAI-NLP51646.2020.9376838

R. Suleesathira

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引用次数: 1

Abstract

This paper provides the capability of the direction of arrival (DOA) identification to determine which the estimated DOA belongs to the desired signal and to undesired signals. One of the well known subspace-based methods for finding directions is MUSIC (MUltiple Signal Classification). The separation of signal and noise subspaces is the crucial step to give the precise estimation. The skewness coefficient is proposed to reinforce the conventional MUSIC method for the subspace division without knowing the number of source signals. The normalized least mean square (NLMS) beamforming is used to compute the weight vector so that it directs the mainbeam towards the desired user. The angle of the mainbeam is identified to be the DOA of the desired signal which makes the rest estimated DOAs belong to interference signals. The application of the DOA identification is shown to be advantageous to the null broadening beamforming. The simulation results confirm the effectiveness of the proposed method in the case of limited snapshots.

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基于MUSIC方法和NLMS波束形成的到达方向识别

本文提供了到达方向(DOA)识别的能力，以确定估计的DOA属于期望信号和不希望信号。其中一种众所周知的基于子空间的寻路方法是MUSIC(多信号分类)。信号和噪声子空间的分离是给出精确估计的关键步骤。提出偏度系数来增强传统MUSIC方法在不知道源信号个数的情况下进行子空间划分的能力。采用归一化最小均方波束形成(NLMS)计算权向量，使主波束指向目标用户。将主波束的角度识别为期望信号的DOA，使其余估计的DOA属于干扰信号。该方法的应用有利于零宽波束形成。仿真结果验证了该方法在有限快照情况下的有效性。

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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2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

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