Abstract
We propose, in this paper, a fully distributed tracking algorithm based on particle flow filter over sensor networks based on the max-consensus. The presented distributed particle flow filter is particularly suitable for the sensor network with limited sensing range and consists of two phases: the estimation phase and consensus phase. The local estimation results are obtained via particle flow filter in the estimation phase; then the sensor nodes agree on the best estimation based on max-consensus protocol in the consensus phase. Numerical simulations and comparisons with other distributed target tracking algorithms are carried out to show the effectiveness and feasibility of our approach.
Highlights
Distributed target tracking focuses on using a group of sensors to collect and process information about environment status
In the fusion center (FC)-based approaches, each sensor node uses the local measurement to estimate the local states by filtering algorithms and transmits the local estimation to a single FC, where a global estimate is calculated based on all the local estimates
We evaluate the performance of the proposed distributed particle flow filter (DPFF) algorithm in the simulated environment and compare it with other approaches: the centralized particle filter (CPF) where we use the performance of the CPF as the base performance, the distributed particle filter based on average consensus [15] (DPF-AV), and the information weight average consensus-based distributed particle filter (DPF-WAV) [12]
Summary
Distributed target tracking focuses on using a group of sensors to collect and process information about environment status. In the realistic scenario with limited sensing capability of sensors, some nodes become naive about the target state at some time instants [7]; the performance of the KCF will deteriorate as each node weighs its neighbors’ estimates in an equal manner To overcome this issue, the generalized Kalman consensus filter (GKCF) [7] was proposed utilizing the weighted averaging consensus. Compared with the particle filter, the particle flow filter can yield a significant reduction of the number of particles especially in the high-dimensional case Another issue of DPFs based on averaged consensus is that they are not suitable for the scenario which there exist some naive nodes in the wireless sensor network (WSN).
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