| 摘要:广义逆波束形成是一种高效的声源识别方法，但是直接求解广义逆所得到重构声源极易受到测量误差的影响，重构声源位置将偏离实际。为提高声源识别精度，本文采用正则化方法控制测量误差，结合奇异值截断滤波正则化和广义逆理论提出一种基于TSVD的广义逆波束形成算法，并建立了数值仿真模型，以单极子点源和扩展性组合声源为研究对象，对比常规波束形成、GIB算法、TSVD-GIB算法详细分析了声源类型与频率等因素对声源识别性能的影响。最后在半消声室内对音箱进行了声源识别验证，结果表明基于TSVD-GIB算法具有较好的稳健性，能有效降低旁瓣干扰， 相比GIB算法能更精准的识别声源。 |
| 关键词: 声源识别 广义逆波束形成 截断奇异值分解 迭代 |
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| Research extended acoustic sources identification based on TSVD generalized inverse beamforming |
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| Abstract:Generalized inverse beamforming(GIB) is a kind of high effective and widely used sound source identification method. However, directly identify the sound source via generalized inverse technology will be seriously affected by measurement error. To improve the performance of sound source identification, a new algorithm based on generalized inverse theory and truncated singular value decomposition(TSVD) was proposed, which is called TSVD-GIB can control the measurement error.Through numerical simulations on single source, extended sources, a comparison between the conventional beamforming, GIB algorithm, TSVD-GIB algorithm is carried to check their qualities of sound source identification affected by factors such as source type and frequency. Finally, the experiment on speakers is implemented in Semi-anechoic chamber to verify the sound source identification, and the result shows that the TSVD-GIB algorithm owns better robustness,which can effectively reduce the side lobe interference and more accurately compared with GIB algorithm. |
| Key words: Sound source identification, Generalized inverse beamforming,Truncated singular value decomposition,Iteration |