Gaussian mixture modeling for source localization

Exploiting prior knowledge, we use Bayesian estimation to localize a source heard by a fixed sensor network. The method has two main aspects: Firstly, the probability density function (PDF) of a function of the source location is approximated by a Gaussian mixture model (GMM). This approximation can theoretically be made arbitrarily accurate, and allows a closed form minimum mean square error (MMSE) estimator for that function. Secondly, the source location is retrieved by minimizing the Euclidean distance between the function and its MMSE estimate using a gradient method. Our method avoids the issues of a numerical MMSE estimator but shows comparable accuracy.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: John T. Flåm, Joakim Jalden, Saikat Chatterjee
• Keywords: Gaussian Mixture Model, ICASSP 2011, Minimum Mean Square Error, Probability Density Function, Signal Processing