A real-valued high-resolution coherent DOA estimation method with unknown source number

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Teng Ma, Minglei Yang, Yu Chen
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引用次数: 0

Abstract

When the signals are coherent and the number of sources is difficult to determine accurately, direction-of-arrival (DOA) estimation becomes challenging. In such scenario, the method proposed in this paper first reconstructs a Toeplitz matrix from the cross-correlation vectors of the array-received signals to perform decorrelation. This decorrelation technique preserves array aperture and facilitates DOA estimation. Subsequently, a new real-valued matrix is constructed using only the real and imaginary parts of the reconstructed matrix, instead of employing a unitary transformation. Based on this real-valued matrix, a synthetic spatial spectrum is formulated using subspace projection theory, requiring only a single matrix inversion and power operation, which improves computational efficiency. Simulation results and theoretical analysis demonstrate the effectiveness of the proposed method for estimating the DOAs of coherent sources in scenarios where the number of sources is unknown.
一种未知源数的实值高分辨率相干DOA估计方法
当信号相参且信号源数量难以准确确定时,到达方向(DOA)估计就变得很有挑战性。在这种情况下,本文提出的方法首先从阵列接收信号的互相关向量重建一个Toeplitz矩阵进行去相关。这种去相关技术既保留了阵列孔径,又便于进行DOA估计。然后,利用重构矩阵的实部和虚部构造一个新的实值矩阵,而不是采用幺正变换。在此实值矩阵的基础上,利用子空间投影理论构造了合成空间谱,只需要一次矩阵反演和幂运算,提高了计算效率。仿真结果和理论分析证明了该方法在源数未知情况下相干源doa估计的有效性。
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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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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