Adaptive Double-resampling Particle Filter Algorithm for Target Tracking
目标跟踪的自适应双重采样粒子滤波算法

Aiming at the problem that particle filter has particle degradation and depletion and the number of particle set is not adaptive to improve filtering accuracy and convergence rate, this paper proposed a adaptive double-resampling partide filter algorithm. This approach can solve the problems mentioned above. The algorithm first uses the observation innovation to determine the resampling of the particle distribution program, then conducts a rcsampling on the basis of the initial resampling .The second sparse resampling reduces the number of particles for updating using a particle mergrog technique.This can improve efficiency in the use of the particles, and avoid the use of excessive particles and increase the amount of calculation.The simulation results based on the DR/GPS show that compared with the traditional PF algorithm, the algorithm is effective to improve the filtering accuracy and stability.