Improved Resampling Procedure Based on Genetic Algorithm in Particle Filter

Particle filtering is a nonlinear and non-Gaussian dynamical filtering system. It has found widespread applications in detection, navigation, and tracking problems. The strong maneuverability of target tracking brings heavy impact on particle attributes in resampling process of particle filters, such as, particle state, particle weights, and so on. This paper proposes a new particle filter algorithm based on genetic algorithm optimization. This algorithm combines the hereditability and aberrance of the genetic algorithm into the resampling procedure of particle filter to improve the adaptability of maneuvering target tracking.