Distributed particle filters for sensor networks

By Mark J. Coates. Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004, 2004

302 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/58684e2f85dedd56b75e16188c23ad97206a3051

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In Artificial Intelligence

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Citations in the corpus

Incremental Adaptive Strategies Over Distributed Networks (2007, background): “In [ 31 ], distributed particle filters that rely on ring, or “chain,” topologies (trees are also possible) are proposed under certain conditions on the probability distributions of the process.”
A Review of Data Fusion Techniques (2013, methodology): “Coates [45] used a distributed particle filter to monitor an environment that could be captured by the Markovian state-space model, involving nonlinear dynamics and observations and non-Gaussian noise.”
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “As a special case, we obtain the result of Connelly (2005) that if a graph can be obtained from Kd+2 by a sequence of d-dimensional 1-extensions, then it is globally rigid in Rd.”
Locating the nodes: cooperative localization in wireless sensor networks (2005, background): “, candidate representations of its coordinates, weighted according to their likelihood [76], [77].”
Algorithms and Protocols for Wireless Sensor Networks (2008, background): “Coates [66] usesfilters for target tracking in cluster-basednetworks inwhich cluster heads perform computations and share information, and the other cluster members sense the environment.”
Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes (2008, background): “Particle filtering methods have been addressed in [23] where each sensor stores representative particles for its location that are weighted according to their likelihood.”