Indexed on: 27 Oct '16Published on: 27 Oct '16Published in: Complex & Intelligent Systems
The particle filtering algorithm was introduced in the 1990s as a numerical solution to the Bayesian estimation problem for nonlinear and non-Gaussian systems and has been successfully applied in various fields including physics, economics, engineering, etc. As is widely recognized, the particle filter has broad application prospects in networked systems, but network-induced phenomena and limited computing resources have led to new challenges to the design and implementation of particle filtering algorithms. In this survey paper, we aim to review the particle filtering method and its applications in networked systems. We first provide an overview of the particle filtering methods as well as networked systems, and then investigate the recent progress in the design of particle filter for networked systems. Our main focus is on the state estimation problems in this survey, but other aspects of particle filtering approaches are also highlighted.