Bayesian Localization in Sensor Networks: Distributed Algorithm and Fundamental Limits
(2010) ICC 2010 - 2010 IEEE International Conference on Communications — Location: Cape Town, South Africa (23.May.2010)
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- Abstract
- Self-localization in ad-hoc sensor networks is becoming a crucial issue for several location-aware applications. This technology implies the combination of absolute anchor locations with relative inter-node information exchanged on a peer-to-peer basis. In this paper we investigate a distributed algorithm and fundamental performance bounds for Bayesian cooperative localization in stochastic networks. Nodes are assumed to be randomly deployed within a finite space according to a prior distribution. Bayesian inference is performed through an iterative local message passing procedure based on belief propagation and particle-filtering message representation. The algorithm performance is analyzed for a simplified scenario in which unknown node positions are randomly scattered along a line segment and anchors are fixed. Global Cramer-Rao bounds are derived and compared to the performance of the distributed algorithm.
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Fontanella, D., Nicoli, M., & Vandendorpe, L. (2010). Bayesian Localization in Sensor Networks: Distributed Algorithm and Fundamental Limits. ICC 2010 - 2010 IEEE International Conference on Communications, 1-5. https://doi.org/10.1109/ICC.2010.5502618