Maximum likelihood wireless sensor network source localization using acoustic signal energy measurements

Abstract

A maximum likelihood (ML) acoustic source location estimation method is presented. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multi-resolution search algorithm and an expectation- maximization (EM) like iterative algorithm are proposed to expediate the computation of source locations. The Cramer-Rao Bound (CRB) of the ML source location estimate has been derived. When there is only a single source in the sensor field, the corresponding CRB formulation can be used to analyze the impacts of sensor placement to the accuracy of location estimates. Extensive simulations have been conducted. Empirically, it is observed that this proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.