Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
New sequential Monte Carlo methods for nonlinear dynamic systems
Statistics and Computing
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Quasi-Monte Carlo filtering in nonlinear dynamic systems
IEEE Transactions on Signal Processing - Part I
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Particle filtering (PF) has been widely used in solving nonlinear/non Gaussian filtering problems. Inferring to the target tracking in a wireless sensor network (WSN), distributed PF (DPF) was used due to the limitation of nodes' computing capacity. In this paper, a novel filtering method -- asynchronous DPF (ADPF) for target tracking in WSN is proposed. There are two keys in the proposed algorithm. Firstly, instead of transferring value and weight of particles, Gaussian mixture model (GMM) is used to approximate the posteriori distribution, and only GMM parameters need to be transferred which can reduce the bandwidth and power consumption. Secondly, in order to use sampling information effectively, when target moving to the next cluster head region, the GMM parameters are transfer to the next cluster head, and combine with the new local GMM parameters to compose the new GMM parameters incrementally. The ADPF can also deal with the situation of different number of nodes in different cluster when using the dynamic cluster structure. The proposed ADPF is compared to some other DPF for WSN target tracking, and the experimental results show that not only the precision is improved, but also the bandwidth and power is reduced.