2017
Vázquez, Manuel A; Míguez, Joaquín
A Robust Scheme for Distributed Particle Filtering in Wireless Sensors Networks Artículo de revista
En: Signal Processing, vol. 131, pp. 190–201, 2017, ISSN: 01651684.
Resumen | Enlaces | BibTeX | Etiquetas: Distributed particle filtering (DPF), Journal, Median posterior, Robust statistics, Sequential Monte Carlo Methods (SMC), Wireless sensors networks (WSNs)
@article{Vazquez2017,
title = {A Robust Scheme for Distributed Particle Filtering in Wireless Sensors Networks},
author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},
url = {http://www.sciencedirect.com/science/article/pii/S016516841630189X},
doi = {10.1016/j.sigpro.2016.08.003},
issn = {01651684},
year = {2017},
date = {2017-02-01},
journal = {Signal Processing},
volume = {131},
pages = {190--201},
abstract = {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.},
keywords = {Distributed particle filtering (DPF), Journal, Median posterior, Robust statistics, Sequential Monte Carlo Methods (SMC), Wireless sensors networks (WSNs)},
pubstate = {published},
tppubtype = {article}
}
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.