Optimal rate allocation for multi-sensor distributed estimation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Distributed scalable multi-target tracking with a wireless sensor network
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Rigidity guided localisation for mobile robotic sensor networks
International Journal of Ad Hoc and Ubiquitous Computing
Consensus based distributed unscented information filtering for air mobile sensor networks
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Distributed target tracking using signal strength measurements by a wireless sensor network
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Energy-efficient collaborative tracking in wireless sensor networks
International Journal of Sensor Networks
Interacting multiple sensor filter
Signal Processing
Distributed filtering over sensor networks for autonomous navigation of UAVs
Intelligent Service Robotics
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Tracking a target in a cluttered environment is a representative application of sensor networks and a benchmark for collaborative signal processing algorithms. This paper presents a strictly decentralized approach to Bayesian filtering that is well fit for in-network signal processing. By combining the sigma-point filter methodology and the information filter framework, a class of algorithms denoted as sigma-point information filters is developed. These techniques exhibit the robustness and accuracy of the sigma-point filters for nonlinear dynamic inference while being as easily decentralized as the information filters. Furthermore, the computational cost of this approach is equivalent to a local Kalman filter running in each active node while the communication burden can be made linearly growing in the number of sensors involved. The proposed algorithms are then adapted to the specific problem of target tracking with data association ambiguity. Making use of a local probabilistic data association, we formulate a decentralized tracking scheme that significantly outperforms the existing schemes with similar computational and communication complexity.