中国物理B ›› 2026, Vol. 35 ›› Issue (5): 54301-054301.doi: 10.1088/1674-1056/ae118d

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Optimized iterative shrinkage threshold generalized inverse beamforming for sound source localization

Huihui He(何辉辉)3, Xinyu Wang(王欣宇)3, Zeyu Yang(杨泽宇)3, Xiaofei Wu(吴晓飞)4, and Shengguo Shi(时胜国)1,2,3,†   

  1. 1 National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China;
    2 Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China;
    3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China;
    4 China Ship Scientific Research Center, Wuxi 214082, China
  • 收稿日期:2025-07-12 修回日期:2025-09-26 接受日期:2025-10-10 发布日期:2026-05-11
  • 通讯作者: Shengguo Shi E-mail:shishengguo@hrbeu.edu.cn
  • 基金资助:
    Project supported by the National Key Scientific Instrument and Equipment Development Projects of China (Grant No. 52327901).

Optimized iterative shrinkage threshold generalized inverse beamforming for sound source localization

Huihui He(何辉辉)3, Xinyu Wang(王欣宇)3, Zeyu Yang(杨泽宇)3, Xiaofei Wu(吴晓飞)4, and Shengguo Shi(时胜国)1,2,3,†   

  1. 1 National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China;
    2 Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China;
    3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China;
    4 China Ship Scientific Research Center, Wuxi 214082, China
  • Received:2025-07-12 Revised:2025-09-26 Accepted:2025-10-10 Published:2026-05-11
  • Contact: Shengguo Shi E-mail:shishengguo@hrbeu.edu.cn
  • Supported by:
    Project supported by the National Key Scientific Instrument and Equipment Development Projects of China (Grant No. 52327901).

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摘要： This work addresses underwater noise source localization. Leveraging the spatial sparsity of sound sources, we employ an optimized iterative shrinkage-thresholding generalized inverse beamforming (OISTA-GIB) method to localize noise sources. Firstly, the sparsity of sound sources is exploited by introducing the $\lambda_{1}$ norm, resulting in an objective function that combines the $\lambda_{1}$ norm, with generalized inverse beamforming. This function is solved using an iterative shrinkage-thresholding algorithm (ISTA) to obtain sound source positions. Secondly, we note that when ISTA solves this objective function, the penalty strength applied by the identity matrix to all scanning points on the sound source surface is uniform. This uniformity reduces positioning accuracy. To enhance localization accuracy and spatial resolution, we propose an iterative regularization matrix-optimized ISTA to solve the objective function. Here, the result from the previous iteration is used to construct a regularization matrix that increases the penalty strength in non-source regions during the current iteration. This process iteratively narrows the mainlobe width in source regions until termination conditions are met, yielding refined sound source positions. Finally, simulations and experimental data processing show that the proposed OISTA-GIB method achieves higher accuracy and spatial resolution in noise source localization compared to existing methods.

关键词: generalized inverse beamforming, iterative regularization matrix, iterative shrinkage threshold, fast iterative shrinkage threshold

Abstract: This work addresses underwater noise source localization. Leveraging the spatial sparsity of sound sources, we employ an optimized iterative shrinkage-thresholding generalized inverse beamforming (OISTA-GIB) method to localize noise sources. Firstly, the sparsity of sound sources is exploited by introducing the $\lambda_{1}$ norm, resulting in an objective function that combines the $\lambda_{1}$ norm, with generalized inverse beamforming. This function is solved using an iterative shrinkage-thresholding algorithm (ISTA) to obtain sound source positions. Secondly, we note that when ISTA solves this objective function, the penalty strength applied by the identity matrix to all scanning points on the sound source surface is uniform. This uniformity reduces positioning accuracy. To enhance localization accuracy and spatial resolution, we propose an iterative regularization matrix-optimized ISTA to solve the objective function. Here, the result from the previous iteration is used to construct a regularization matrix that increases the penalty strength in non-source regions during the current iteration. This process iteratively narrows the mainlobe width in source regions until termination conditions are met, yielding refined sound source positions. Finally, simulations and experimental data processing show that the proposed OISTA-GIB method achieves higher accuracy and spatial resolution in noise source localization compared to existing methods.

Key words: generalized inverse beamforming, iterative regularization matrix, iterative shrinkage threshold, fast iterative shrinkage threshold

中图分类号:  (Acoustic signal processing)

  • 43.60.+d
43.58.+z (Acoustical measurements and instrumentation) 43.50.+y (Noise: its effects and control)