Parallel Genetic Unscented Particle Filter Algorithm

For Particle filter (PF) has the problem of degeneracy phenomenon, this paper effectively integrates Parallel genetic algorithms (PGAs) and Unscented particle filter (UPF), and puts forward the Parallel genetic unscented particle filter (PGUPF) based on distributed algorithm structure. This method divides all particles into several subpopulations to parallel execute particle filtering. Based on importance density optimization by Unscented Kalman filter (UKF), several genetic operators such as crossover, mutation, selection and migration are adopted to optimize importance sampling and resampling processes, which can not only move particles fast to the region of high likelihood, but also increase particles diversity. It gives full play to the characteristic of parallelization of PF, and decreases computation amount of the filtering algorithm. By theoretical analysis and software simulation, PGUPF is compared with several existed PF algorithms in the estimation accuracy and computation efficiency for satellite navigation and positioning, and the effectiveness of PGUPF has been verified.