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基于奇异值分解和改进加权的时延估计优化算法。

Optimization Algorithm for Delay Estimation Based on Singular Value Decomposition and Improved - Weighting.

机构信息

Academy of Weapony Engineering, Naval University of Engineering, Wuhan 430033, China.

Academy of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China.

出版信息

Sensors (Basel). 2022 Sep 24;22(19):7254. doi: 10.3390/s22197254.

DOI:10.3390/s22197254
PMID:36236355
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9571281/
Abstract

The accuracy of time delay estimation seriously affects the accuracy of sound source localization. In order to improve the accuracy of time delay estimation under the condition of low SNR, a delay estimation optimization algorithm based on singular value decomposition and improved - weighting (--ργ weighting) is proposed. Firstly, the acoustic signal collected by the acoustic sensor array is subjected to singular value decomposition and noise reduction processing to improve the signal-to-noise ratio of the signal; then, the cross-correlation operation is performed, and the cross-correlation function is processed by the --ργ weighting method to obtain the cross-power spectrum; finally, the inverse transformation is performed to obtain the generalized correlation time domain function, and the peak detection is performed to obtain the delay difference. The experiment was carried out in a large outdoor pool, and the experimental data were processed to compare the time delay estimation performance of three methods: - weighting, -- weighting (meaning: - weighting based on singular value decomposition) and ---ργ weighting (meaning: --ργ weighting based on singular value decomposition). The results show that the delay estimation optimization algorithm based on ---ργ improves the delay estimation accuracy by at least 37.95% compared with the other two methods. The new optimization algorithm has good delay estimation performance.

摘要

时延估计的准确性严重影响声源定位的准确性。为了提高低信噪比条件下的时延估计精度，提出了一种基于奇异值分解和改进的 - ργ 加权（--ργ 加权）的时延估计优化算法。首先，对声传感器阵列采集的声信号进行奇异值分解和降噪处理，以提高信号的信噪比；然后，进行互相关运算，并采用 --ργ 加权法对互相关函数进行处理，得到互功率谱；最后，进行逆变换，得到广义相关时域函数，并进行峰值检测，得到时延差。在大型室外水池中进行了实验，对三种方法（-ργ 加权、--加权（表示：基于奇异值分解的 - ργ 加权）和 ---ργ 加权（表示：基于奇异值分解的 --ργ 加权））的时延估计性能进行了处理和比较。结果表明，与其他两种方法相比，基于 ---ργ 的时延估计优化算法至少提高了 37.95%的时延估计精度。新的优化算法具有良好的时延估计性能。

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A fusion of principal component analysis and singular value decomposition based multivariate denoising algorithm for free induction decay transversal data.一种基于主成分分析和奇异值分解的用于自由感应衰减横向数据的多元去噪算法。
Rev Sci Instrum. 2019 Mar;90(3):035116. doi: 10.1063/1.5089582.