@PHDTHESIS{Lehmann04Particle,
  author =   {Eric A. Lehmann},
  title =    {Particle Filtering Methods for Acoustic Source Localisation and Tracking},
  school =   {Research School of Information Sciences and Engineering, Department of Telecommunications Engineering},
  address =  {The Australian National University, Canberra, ACT, Australia},
  month =    {July},
  year =     {2004},
  abstract = {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 different 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 developments attempt to improve the basic particle filtering technique. Three different methods using the concept of sequential importance sampling are proposed, and their respective performance is also tested experimentally. The practical results demonstrate the strengths of the new approach. Using importance sampling, the valuable property of reinitialisation is integrated at a low algorithm level. Despite yielding a slightly lower tracking accuracy, these methods are able to automatically recover from complete track losses, detect new targets entering the acoustic scene, and switch between alternating talkers. It is found that particle filters based on the importance sampling principle are better suited for practical applications than the filters developed previously. This work also presents a theoretical performance analysis of acoustic source tracking methods. Theoretical limits on the estimation error are derived based on the posterior Cramér-Rao bound. To this purpose, two mathematical observation models are developed that describe how source localisation measurements are obtained in practice. These models are derived from statistical room acoustics principles. The influence of the correlation existing between sound intensity values measured in a diffuse sound field is investigated in detail. A comparison of two generic particle filters with respect to the derived lower error bound is presented. Whereas the performance of the tested algorithms is clearly influenced by the level of sound intensity correlation, simulation results show that the posterior Cramér-Rao bound is not affected by it. These results hence point out that this type of estimation error bound may not be fully appropriate for a practical consideration of the acoustic source localisation problem.}
}