Multiple acoustic source localization based on multiple hypotheses testing using particle approach

Localization of multiple acoustic sources in a non-ideal environment has a number of difficulties, among which are accurate acoustic feature estimation for multiple sources and association uncertainty between measurements and their corresponding sources. This paper focuses more on the latter and proposes an algorithm based on a multiple-hypothesis framework for both a measurement model and a measurement association model to localize multiple sources. A conditional data likelihood model based on a measurement hypothesis is proposed and implemented using particles. Simulation results demonstrate that the proposed algorithm is capable of localizing the positions of multiple sources with a small number of microphones without any prior knowledge when the amount of reverberation is moderate.