Particle filter resampling based on optimized combinatorial algorithm

In particle Alter algorithm, the resampling step effectively solves the problem of particles degeneracy; however, it reduces the particle variety. This article describes how to use chaos, immunity algorithm and genetic algorithm carried on particle resampling corrective method. We present a novel algorithm which combines immune algorithm, chaos and genetic algorithm. This immune genetic algorithm based on chaos initializes cluster by the over-spread character and randomicity of chaos to improve search speed and renews cluster by chaos sequence and enhancing cluster diversity to avoid local optimization. Chaos also is adopts to optimize the local optimization to increase precision. After crossover and mutation, using chaotic local optimization near the optimal solution to enhance the precision of the solutions. The experimental results show that it has the quicker convergence rate and the better iterative estimating capability, compared with the particle resampling based on the immunity genetic algorithm.