关键词: 粒子滤波,重要性密度函数,集合卡尔曼滤波,组合定位系统

Abstract: An improved particle filtering algorithm based on the ensemble Kalman filter (EnKF) was proposed in this paper starting with the selection of importance density function of the particle filter.At each time instant,the importance density function is generated by EnKF which fuses the latest observation information and propagates the system states by using a collection of sampled state vectors,called an ensemble.In this way,the importance density function can be very close to the true posterior probability.Furthermore,to avoid the particle impoverishment problem,the Markov Chain Monte Carlo method was introduced after resampling process.In the simulation,the developed filter was compared with standard particle filter,extended Kalman particle filter and unscented particle filter in GPS/DR integrated system.The simulation results demonstrate the validity of the developed algorithm.Under the same conditions,the new filter is superior to other particle filtering algorithms with the respect to estimation accuracy,as well as it controls the computational load effectively.It is also found that the new filter can obtain outstanding performance even with a small number of particles.

Key words: Particle filter,Importance density function,Ensemble kalman filter,Integrated localization system