基于虚阵插值的准平稳信号欠定DOA估计

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Kangning Li , Qing Shen , Wei Liu , Zexiang Zhang , Tianyuan Gu , Wei Cui
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引用次数: 0

摘要

提出了一种基于虚拟阵列插值的准平稳信号欠定到达方向估计方法。首先构造了基于准平稳信号的二阶差分共阵模型。然后将该模型内插到均匀线性阵列(ULA)中。不是单独处理每个时间框架,而是同时在所有时间框架中应用单个矩阵完成操作。该方法利用信号的拟平稳性和自协方差矩阵的低秩性进行矩阵补全。提出了一种基于乘法器交替方向法(ADMM)的求解矩阵补全问题的方法,该方法比常用的半确定规划(SDP)框架更有效。然后,利用子空间法对完成的协方差矩阵进行DOA估计。通过与现有的基于插值的QSS方位估计方法的对比分析,表明本文方法具有较高的精度和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Underdetermined DOA estimation of quasi-stationary signals via virtual array interpolation
An underdetermined direction of arrival (DOA) estimation method for quasi-stationary signals (QSSs) using virtual array interpolation is proposed. A second-order difference co-array model based on quasi-stationary signals is first constructed. This model is then interpolated into a uniform linear array (ULA). Instead of processing each time frame individually, a single matrix completion operation is applied across all time frames simultaneously. This method leverages the quasi-stationarity of the signals and the low-rank property of the auto-covariance matrix for matrix completion. An alternating direction method of multipliers (ADMM) based solution is introduced to solve the matrix completion problem, which is more efficient than the commonly used semi-definite programming (SDP) framework. Subsequently, the subspace method is utilized on the completed covariance matrix for DOA estimation. Comparative analysis with the existing interpolation-based QSS DOA estimation method demonstrates that the proposed method achieves superior accuracy and efficiency.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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