Particle filter algorithm based on evolution sampling

In particle filter algorithm, the re-sampling step effectively solves the problem of particles degeneracy, however, it reduces the particle variety. An improved particle filtering algorithm is given based on the evolution sampling. In the process of re-sampling, this algorithm generates candidate particles based on the Markov-Chain-Monte-Carlo (MCMC) technique and the analog binary crossover principle, and then, weighs the sampling particles against their importance according to the fitness function. The current re-sampling particles are then associated in constructing the candidate particle set to enhance the variety of re-sampling particles. Finally, the optimizing selection of particles is realized based on the particle weigh. Simulation results show the method can effectively improve the state estimation precision.