Multi-source localization based on approximated kernel density estimator and spatial likelihood function in near-field reverberant environment

Yuzhuo Fang, Xu Zhi-yong, Zhao Zhao
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Abstract

In order to cope with the multi-source localization in near-field reverberant environment, approximated kernel density estimator (KDE) algorithm is introduced to provide robust anti-reverberation performance and multi-stage (MS) is used to solve the spectrum aliasing of high frequency on account of wide spacing of microphone array. Then spatial likelihood function (SLF) is built to mix the pairwise KDE or KDEMS function together. Based on the above KDE, MS, SLF, two algorithms SLF-KDE, SLF-KDEMS is proposed. The feasibility of the methods is confirmed by theoretical derivation and computer simulation. The results shows that SLF-KDEMS is a localization algorithm with high robustness and recognition in near-field reverberant environment.
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近场混响环境下基于近似核密度估计和空间似然函数的多源定位
为了解决近场混响环境下的多源定位问题,引入近似核密度估计(KDE)算法提供鲁棒的抗混响性能,并采用多级(MS)算法解决传声器阵列间距大导致的高频频谱混叠问题。然后建立空间似然函数(SLF),将KDE和kdem函数混合在一起。在上述KDE、MS、SLF算法的基础上,提出了SLF-KDE、SLF- kdem两种算法。理论推导和计算机仿真验证了方法的可行性。结果表明,slf - kdem是一种在近场混响环境下具有较高鲁棒性和识别率的定位算法。
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