Robust maximum likelihood acoustic source localization in wireless sensor networks

Authors:
Yong Liu;Yu Hen Hu;Quan Pan
Affiliations:
School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R.China;Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI;School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R.China
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

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Abstract

Sensor measurements in a wireless sensor network (WSN) may significantly deviate from a commonly used Gaussian noise model due to harsh operating conditions, unreliable wireless communication links, or sensor failures. In this work, a mixed Gaussian and impulse noise model is proposed to more accurately model these types of non-Gaussian noise. However, existing maximum likelihood (ML) acoustic energy based source localization algorithms are very sensitive to non-Gaussian noise perturbations. To mitigate this shortcoming, a novel M-estimate based robust estimation formulation is derived. Extensive simulation results demonstrated superior and consistent performance advantage of this robust estimation approach compared to conventional ML estimates over a wide range of practical scenarios.