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 configurati...

• Type: Conference
• Year: 2009
• Where: ACCV
• Authors: Xiaoqin Zhang, Weiming Hu, Steve J. Maybank
• Keywords: ACCV 2009, Computer Vision, Importance Sampling, Particle filters, Swarm Intelligence