A robust scheme for distributed particle filtering in wireless sensors networks Articles uri icon

publication date

  • February 2017

start page

  • 190

end page

  • 201


  • 131

International Standard Serial Number (ISSN)

  • 0165-1684

Electronic International Standard Serial Number (EISSN)

  • 1872-7557


  • Wireless sensor networks (WSNs) have become a popular technology for a broad range of applications where the goal is to track and forecast the evolution of time-varying physical magnitudes. Several authors have investigated the use of particle filters (PFs) in this scenario. PFs are very flexible, Monte Carlo based algorithms for tracking and prediction in state-space dynamical models. However, to implement a PF in a WSN, the algorithm should run over different nodes in the network to produce estimators based on locally collected data. These local estimators then need to be combined so as to produce a global estimator. Existing approaches to the problem are either heuristic or well-principled but impractical (as they impose stringent conditions on the WSN communication capacity). Here, we introduce a novel distributed PF that relies on the computation of median posterior probability distributions in order to combine local Bayesian estimators (obtained at different nodes) in a way that is efficient, both computation and communication-wise. An extensive simulation study for a target tracking problem shows that the proposed scheme is competitive with existing consensus-based distributed PFs in terms of estimation accuracy, while it clearly outperforms these methods in terms of robustness and communication requirements. (C) 2016 Elsevier B.V. All rights reserved.


  • wireless sensors networks (wsns); distributed particle filtering (dpf); sequential monte carlo methods (smc); robust statistics; median posterior; convergence; tracking