基于特征值聚类的MUSIC算法

Q3 Engineering
Mingyang Zhang, Songyuan Zha, Yudong Liu
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引用次数: 0

摘要

传统的MUSIC算法需要提前知道目标信号源的数量,并进一步确定信号子空间和噪声子空间的维数,最后搜索频谱峰值。在工程中,不可能预测要测量的目标信号源的数量。为了解决上述问题,提出了一种不估计目标信号源数量的改进MUSIC算法。在现有算法中,协方差矩阵的所有特征向量都被视为噪声子空间进行谱估计,但信号子空间的存在会使结果不可靠。为了使估计结果更加准确,提出了一种新的对噪声子空间和信号子空间的谱估计结果进行加权的方法。仿真结果表明,在信号源数量未知的情况下,改进算法能够准确估计信号源的数量和方向,比传统的MUSIC算法具有更大的实用性。此外,改进后的算法具有更好的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MUSIC algorithm based on eigenvalue clustering
The traditional MUSIC algorithm needs to know the number of target signal sources in advance, and further determine the dimensions of signal subspace and noise subspace, and finally search for spectral peaks. In engineering, it is impossible to predict the number of target signal sources to be measured. To solve the above-mentioned problem, an improved MUSIC algorithm without estimating the number of target signal sources is proposed. In the present algorithm, all eigenvectors of covariance matrix are regarded as noise subspace for spectral estimation, but the existence of signal subspace will make the result unreliable. In order to make the estimation result more accurate, a new weighting method for the spectral estimation results of noise subspace and signal subspace is proposed. The simulation results show that the improved algorithm can accurately estimate the number and direction of signal sources when the number of signal sources is unknown, and has greater practicability than the traditional MUSIC algorithm. In addition, the improved algorithm has better robustness.
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来源期刊
西北工业大学学报
西北工业大学学报 Engineering-Engineering (all)
CiteScore
1.30
自引率
0.00%
发文量
6201
审稿时长
12 weeks
期刊介绍:
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