Particle filter parallelisation using random network based resampling

The particle filter approximation to the posterior density converges to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational load, which can be implemented by operating the particle filter in parallel architectures. However, the resampling stage in the particle filter requires synchronisation, extensive interchange and routing of particle information, and thus impedes the use of parallel hardware systems. This paper presents a novel resampling technique using a fixed random network. This idea relaxes the synchronisation constraints and minimises the particle interaction to a significant level. Using simulations we demonstrate the validity of our technique to track targets in linear and non-linear sensing scenarios.