Central Difference Kalman-probability Hypothesis Density Filter for Multiple Speakers Tracking
Liming Chen · Journal of Information and Computational Science · 2014
Aiming at nonlinear system model in multiple speakers tracking, a central difference Kalman-probability hypothesis density filter for multiple speakers tracking is proposed in this paper. Time difference of arrival for microphone array is taken as measurement, Stirling interpolation formula is utilized to derive polynomial approximations of nonlinear functions, central difference Kalman filter and Gaussian mixture probability hypothesis density filter are applied to estimate first-order statistical moment of posterior multiple speakers states, and finally multiple speakers tracking of nonlinear Gaussian system is realized while the speakers states are extracted by recursive updating. Simulation results show that the robustness of the algorithm is enhanced, and estimation accuracy of multiple speakers number and states is improved.