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Robust Interval-Based Localization Algorithms for Mobile Sensor Networks

Abstract : This paper considers the localization problem in mobile sensor networks. Such a problem is a challenging task, especially when measurements exchanged between sensors may contain outliers, \textit{i.e.}, data not matching the observation model. This paper proposes two algorithms robust to outliers. These algorithms perform a set-membership estimation, where only the maximal number of outliers is required to be known. Using these algorithms, estimates consist of sets of boxes whose union surely contains the correct location of the sensor, provided that the considered hypotheses are satisfied. This paper proposes as well a technique for evaluating the number of outliers to be robust to. In order to corroborate the efficiency of both algorithms, a comparison of their performances is performed in simulations using Matlab.
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Contributor : Michel Kieffer Connect in order to contact the contributor
Submitted on : Tuesday, September 4, 2012 - 1:24:15 PM
Last modification on : Tuesday, December 7, 2021 - 4:10:19 PM
Long-term archiving on: : Wednesday, December 5, 2012 - 10:13:32 AM


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Farah Mourad-Chehade, Hichem Snoussi, Michel Kieffer, Cédric Richard. Robust Interval-Based Localization Algorithms for Mobile Sensor Networks. International Journal of Distributed Sensor Networks, Hindawi Publishing Corporation, 2012, 2012, Article ID 303895, 7 p. ⟨10.1155/2012/303895⟩. ⟨hal-00727777⟩



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