@inproceedings{004376d3e10a40ffaa78e0c74ad68127,
title = "Robust maximum likelihood acoustic source localization in wireless sensor networks",
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.",
keywords = "Acoustic energy, M-estimate, Maximum likelihood, Robust statistics, Sensor network, Source localization, Target tracking",
author = "Yong Liu and Hu, {Yu Hen} and Quan Pan",
year = "2009",
doi = "10.1109/GLOCOM.2009.5426166",
language = "英语",
isbn = "9781424441488",
series = "GLOBECOM - IEEE Global Telecommunications Conference",
booktitle = "GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference",
note = "2009 IEEE Global Telecommunications Conference, GLOBECOM 2009 ; Conference date: 30-11-2009 Through 04-12-2009",
}