Distributed implementations of particle filters
Anwer S. Bashi, Vesselin P. Jilkov, X.R. Li, Huimin Chen · 2003
Particle filtering has a great potential for solving highly nonlinear and non-Gaussian estimation problems, generally intractable within a standard linear Kalman filtering based framework. However, the implementation of particle filters (PFs) is rather computationally involved, which nowadays prevents them from practical real-world application. A natural idea to make PFs feasible for "real-time" data processing is to implement them on distributed multiprocessor computer systems. This paper presents three schemes for distributing the computations of generic particle filters, including resampling and, optionally, a Metropolis-Hastings (MH) step. Simulation results based on a maneuvering target tracking scenario show that distributed implementations can provide a promising solution to the steep computational burden incurred when using a large number of particles.