Gravity optimized particle filter algorithm

As the traditional particle filter has problems of particle degeneracy and particle diversity loss and filter accuracy depends heavily on the particle number, a gravity optimized particle filter algorithm is proposed.The particle swarm is optimized by the gravity algorithm in the particle filter to improve the filtering accuracy. Each particle is regarded as a mass point and the mass is proportional to the particle weight.The gravity attracts particles moving toward the high likelihood region which optimizes the particle swarm.Then elite particle strategy is introduced to accelerate the particle convergence rate and avoid the local optimum in the gravity algorithm. The perceptual model is used to prevent particles from crowding or overlapping due to excessive convergence. Simulation results show that the proposed algorithm has a better filtering accuracy and speed in the case of few particles compared with the classical particle filter algorithm and particle swarm optimization particle filter algorithm.