Consensus-Based Particle Filter

The particle filter is well known as a state estimation method for nonlinear and non-Gaussian system. However, particle filter has the inherent drawbacks such as samples less of diversity and low tracking accuracy. In this paper, a novel particle filter algorithm with the Markov Chain Monte Carlo (MCMC) and consensus strategy is proposed. The authors utilize MCMC sampling method to make the particles more diversification. And the algorithm is optimized by consensus strategy, which makes the state estimates of all network nodes converge to a more precise value. Simulation results show that compared to existing methods, the proposed algorithm has superior performance.