Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
GPS-Free Positioning in Mobile ad-hoc Networks
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 9 - Volume 9
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
IEEE Communications Magazine
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
Learning Network Topology from Simple Sensor Data
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Sensor self-localization with beacon position uncertainty
Signal Processing
Hybrid inference for sensor network localization using a mobile robot
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Inferring a probability distribution function for the pose of a sensor network using a mobile robot
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
LabRatTM: miniature robot for students, researchers, and hobbyists
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Evolutionary constrained self-localization for autonomous agents
Applied Soft Computing
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This paper describes a technique for the probabilistic self-localization of a sensor network based on noisy inter-sensor range data. Our method is based on a number of parallel instances of Markov Chain Monte Carlo (MCMC). By combining estimates drawn from these parallel chains, we build up a representation of the underlying probability distribution function (PDF) for the network pose. Our approach includes sensor data incrementally in order to avoid local minima and is shown to produce meaningful results efficiently. We return a distribution over sensor locations rather than a single maximum likelihood estimate. This can then be used for subsequent exploration and validation.