The task of acoustic source tracking plays an important role in many practical speech acquisition systems. This research presents an extensive study of sequential Monte Carlo methods applied to the source localisation problem, based on the signals received at an array of microphones. A general framework for acoustic source localisation using particle filtering is proposed, and four di#erent algorithms that fit within this framework are subsequently developed. To assess the performance of these new methods, statistical simulations are carried out using both synthetic and real-life samples of audio data. The simulation results demonstrate the superiority of an approach based on sequential estimation. The resulting particle filters are shown to drastically outperform traditional acoustic source localisation methods. Further