Improved particle filtering techniques based on generalized interactive genetic algorithm

This paper improves the resampling step of particle filtering (PF) based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage. For target tracking in image processing, this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image. The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics, and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation, and non-uniform mutation operator is used to capture all kinds of mutation in this paper. The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering (EKF), PF, regularized partide filtering (RPF), and genetic algorithm (GA)-PF.