Matrix analysis
Decentralized Estimation and Control for Multisensor Systems
Decentralized Estimation and Control for Multisensor Systems
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
A distributed algorithm for managing multi-target identities in wireless ad-hoc sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Decentralized sensor fusion with distributed particle filters
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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This paper considers the problem of distributed particle filtering using consensus algorithms. The monitored environment may possess nonlinear dynamics, nonlinear measurements, and non-Gaussian process and observation noises. It considers the scenario in which a set of sensor nodes make multiple, noisy measurements of the monitored system. The goal of the proposed approach is to perform an on-line, distributed estimation of the current state at multiple sensor nodes. In this new proposed algorithm, average consensus filters are well organized to do distributed computation and information consensus in distributed particle filtering. Furthermore, sensors' energy consumption concerns are considered partially here. In order to achieve almost full environment information, sensors are assumed to have different sensing models, but same dimensions. As a case study, the application of the proposed algorithm to state estimation of an unmanned air vehicle is considered here. Simulation results show the good efficiency of the algorithm in the nonlinear state estimation.