Abstract
This paper proposes an improved Gaussian particle filter integratingthe Artificial Fish School Algorithm to optimise the measured values to improve the overall estimation accuracy of the system. Meanwhile, it also solves the problems of susceptibility to interference and insufficient estimation accuracy in nonlinear systems. Furthermore, since the calculation time of the fusion algorithm increases, in order to ensure the speed of state estimation, the linear transformation of standard particle swarm is used to replace the particle sampling link of Gaussian particle filter. Simulation results show that the calculation speed of a fast Gaussian Particle Filter based on the Artificial Fish School Algorithm is 21.7% faster than the Particle Filter based on the Artificial Fish School Algorithm. Compared with Particle Filter, Gaussian particle filter, and the Artificial Fish School Algorithm, the proposed algorithm has a higher accuracy.
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