Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highl...Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.展开更多
为提升反卷积算法的计算效率,提出一种压缩聚焦网格点的快速反卷积算法。该算法基于函数波束形成的输出,根据设定的声源识别阈值,压缩参与反卷积算法循环的聚焦网格点数。算法融合了函数波束形成与相干声源图清晰算法CLEAN-SC(CLEAN bas...为提升反卷积算法的计算效率,提出一种压缩聚焦网格点的快速反卷积算法。该算法基于函数波束形成的输出,根据设定的声源识别阈值,压缩参与反卷积算法循环的聚焦网格点数。算法融合了函数波束形成与相干声源图清晰算法CLEAN-SC(CLEAN based on spatial source coherence)的优点,可进一步提高多声源定位的空间分辨率,并有效降低算法计算时间。仿真和试验表明:所提算法对低于瑞利极限的不相干多声源具有良好的识别效果;试验中,与CLEAN-SC相比,所提算法的计算效率提升了约3.90倍。展开更多
基金supported by the National Science and Technology Major Project of China (No. 2017-II-003–0015)。
文摘Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.
文摘为提升反卷积算法的计算效率,提出一种压缩聚焦网格点的快速反卷积算法。该算法基于函数波束形成的输出,根据设定的声源识别阈值,压缩参与反卷积算法循环的聚焦网格点数。算法融合了函数波束形成与相干声源图清晰算法CLEAN-SC(CLEAN based on spatial source coherence)的优点,可进一步提高多声源定位的空间分辨率,并有效降低算法计算时间。仿真和试验表明:所提算法对低于瑞利极限的不相干多声源具有良好的识别效果;试验中,与CLEAN-SC相比,所提算法的计算效率提升了约3.90倍。
