זיהוי מיקום מבוסס particle filter (PF) של דובר בסביבה רועשת ומהדהדת

Speech Source Localization in Noisy and Reverberant Environment using the EKF
by Hanan Ashwega & Nir Russo

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a state-space approach using particle Kalman filtering (PKF) [1]. Using a sequential Monte Carlo method, particle filters are used to recursively estimate the probability density of the unknown source location conditioned on all received data up to and including the current frame. Ward et al. [2] proposed the use of a particle Kalman filter in conjunction with a beamformer to estimate the speaker position in a one-stage procedure. Vermaak and Blake [3] consider the reverberation through a bi-modal distribution of the noisy measurement around the true TDOA. Utilizing this distribution and giving a first-order Markov process model for the speaker trajectory, a particle Kalman filter is derived and applied to the problem at hand. Lehmann and Williamson [4] also use the particle Kalman filter. However they incorporate the importance sampling (IS) concept, in which particles are generated in each time step based on the previous time step and the current measurement. The importance function is implemented based on a delay-and-sum beamforming results.