Acoustic source tracking using multiple weighted peaks of the localization function
G. P. Yang, Qi Cheng, Yao Guo, Hongyan Zhu · 2017
In the presence of strong noise or reverberation, the acoustic source localization and tracking problem is confronted with severe challenges. One of them is that the maximal peak of the localization function may not be generated by the real source. In this paper, the stochastic region contraction (SRC) is adopted to search for multiple peaks of the localization function rather than the maximal one only, so as to provide sufficient pseudo-measurement information. A mixed distribution model is used to describe the pseudo-measurement likelihood function within the particle filter framework, where the mixing weight is evaluated based on the idea of probability data association (PDA) by incorporating the dynamic information of the sound source. Simulation results demonstrate that the proposed approach can effectively improve the ability of anti-noise and anti-reverberation of the acoustic target tracking system.