目标跟踪的自适应双重采样粒子滤波算法

Adaptive Double-resampling Particle Filter Algorithm for Target Tracking

针对传统粒子滤波存在的粒子退化、贫化问题及粒子集个数不能自适应改变带来的滤波精度和收敛速度下降的问题,提出一种基于双重采样的自适应粒子滤波算法。该算法首先利用观测新息来确定重采样粒子分布方案,然后在首次重采样基础上,采用粒子交叉聚合算法进行二次重采样,提高了粒子的使用效率,避免了由于使用过多粒子而增加计算量的问题。基于DR/GPS的实验仿真结果表明,与传统的PF算法相比,该算法有效提高了滤波精度和稳定性。

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-resarnpling par- ticle 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 re-sampling on the basis of the initial resampling. The second sparse resampling reduces the number of particles for updating using a particle mer- ging technique. This can improve efficiency in the use of the particles, and avoid the use of excessive particles and in- crease 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.