A Smarter Particle Filter

Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is based on importance sampling. However, in the literature, the proper choice of the proposal distribution for importance sampling remains a tough task and has not been resolved yet. Inspired by the animal swarm intelligence in the evolutionary computing, we propose a swarm intelligence based particle filter algorithm. Unlike the independent particles in the conventional particle filter, the particles in our algorithm cooperate with each other and evolve according to the

cognitive effect

and

social effect

in analogy with the cooperative and social aspects of animal populations. Furthermore, the theoretical analysis shows that our algorithm is essentially a conventional particle filter with a hierarchial importance sampling process which is guided by the swarm intelligence extracted from the particle configuration, and thus greatly overcome the sample impoverishment problem suffered by particle filters. We compare the proposed approach with several nonlinear filters in the following tasks: state estimation, and visual tracking. The experiments demonstrate the effectiveness and promise of our approach.