基于两阶段累积量的广义MUSIC被动混合源定位算法

A. Ebrahimi, H. R. Abutalebi, M. Karimi
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引用次数: 7

摘要

在这项研究中,作者使用均匀线性阵列(ULA)解决了被动混合近场和远场源定位问题,其中阵列接收的信号可能来自混合源。本文提出了一种新的基于两级累积量的多信号分类(MUSIC)算法,该算法利用ULA数据的四阶累积量进行被动源定位。该算法的显著特点是构造了一个新的特殊的累积矩阵来获取ULA接收到的信号的更多信息。因此,该算法具有较高的到达方向(DOA)和距离估计精度,并减轻了阵列孔径损失。通过蒙特卡罗仿真验证了该方法在提高到达方向和距离估计精度方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalised two stage cumulants-based MUSIC algorithm for passive mixed sources localisation
In this study, the authors address the problem of passive mixed near-field and far-field sources localisation using a uniform linear array (ULA) in which the signals received by the array may come from mixed sources. This study presents a new two stage cumulant-based multiple signal classification (MUSIC) algorithm for passive source localisation using fourth-order cumulants of a ULA data. The significant characteristic of the proposed algorithm is that it constructs a new special cumulant matrix to acquire more information of signals received by a ULA. Consequently, the proposed algorithm gives high direction of arrival (DOA) and range estimation accuracy, and alleviates the array aperture loss. Monte Carlo simulations are established to verify the effectiveness of the proposed method in increasing direction of arrival and range estimation accuracies.
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