Stochastic particle filtering: A fast SRP-PHAT single source localization algorithm

Computational cost has been an issue for the proven robust source localization algorithm, steered response power (SRP) using the phase transform (SRP-PHAT). Some proposed computation reduction algorithms degrade under high noise and reverberant conditions. Some require at least 10% the cost of a full SRP-PHAT gridsearch. In ICASSP 2007, we introduced a robust, low-cost global optimization technique, stochastic region contraction (SRC). In this paper, we present another algorithm, stochastic particle filtering (SPF), which uses SRC´s initialization and is a kind of Importance Sampling technique. In this paper, the SRP is computed using a modification to the conventional PHAT, namely ß-PHAT. Extensive experiments using real data and simulated data are shown. The results indicate that, while maintaining the desirable accuracy of the full search, this method reduces the cost to about half the cost of SRC (0.03% the cost of full search), thus making SRP-PHAT more practical for real-time applications.