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一种用于混响环境中目标定位的高分辨率时间反转方法。

A High-Resolution Time Reversal Method for Target Localization in Reverberant Environments.

作者信息

Ma Huiying, Shang Tao, Li Gufeng, Li Zhaokun

机构信息

State Key Laboratory of Integrated Service Network, Xidian University, Xi'an 710071, China.

Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an 710071, China.

出版信息

Sensors (Basel). 2024 May 17;24(10):3196. doi: 10.3390/s24103196.

DOI:10.3390/s24103196
PMID:38794050
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11124834/
Abstract

Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust.

摘要

实际环境中的混响是影响目标声源定位(SSL)方法高分辨率的重要因素。宽带低频信号在实际环境中很常见。本研究聚焦于此类信号在混响环境中的定位。由于时间反转(TR)方法能够克服多径效应并实现自适应聚焦,因此特别适用于混响环境中的声源定位。基于稀疏贝叶斯学习算法在波方向估计方面的显著优势,提出了一种适用于混响环境的新型声源定位方法。首先,研究了混响环境中的声音传播模型并获得了TR聚焦信号。然后,利用稀疏贝叶斯框架对宽带低频声源进行定位。为验证所提方法在混响环境中对宽带低频目标定位的有效性,进行了仿真和实际数据实验。在仿真实验中研究了不同带宽、不同麦克风数量、信噪比、混响时间和非网格条件下的定位性能。实际实验在混响室中进行。仿真和实验结果表明,所提方法在混响环境中能够实现令人满意的空间分辨率且具有鲁棒性。

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本文引用的文献

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