Abstract
Particle filter based on particle swarm optimization (PSO-PF) is not precise enough and trapping in local optimum easily, it is not able to meet the requirement of modern navigation system. To solve the problems, a new particle filter based on dynamic clone particle swarm optimization (DPSO-PF) is presented in this paper. This improved filter enables the particles to fit the condition better and then reach the goal of global optimization through orthogonal initialization, clonal selection and local searching of self-learning, accordingly a best balance is achieved between optimal exploring and convergence rate. Finally univariate nonstationary growth model and integrated navigation model are used for simulation experiment and the results indicate that this new filter improves the precision of GPS/INS integrated navigation system.
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