Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

TLDR

The study presents a maximum likelihood acoustic source location estimation method for wireless ad hoc sensor networks. The method uses acoustic energy measurements at individual sensors, employing a multiresolution search and EM‑like iterative algorithm to estimate multiple source locations, and analyzes sensor placement effects via the CRB. Simulations and real‑world experiments show that the proposed ML method consistently outperforms existing acoustic energy‑based source localization techniques, delivering more accurate multi‑source localization and achieving a derived CRB.

Abstract

A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. 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 the 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 multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the 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.