원문정보
초록
영어
In view of the problem of particle degradation and tracking accuracy in the standard particle filter tracking target algorithm, a new improved particle filter algorithm called Iterated Cubature Kalman Particle Filter (ICKPF) is proposed in this paper. The new ICKPF algorithm is based on the Markov Chain Monte Carlo (MCMC), and the cubature rule based on numerical integration method is used to calculate the mean and covariance, which generates the proposal distribution for the particle filter. The current measurements are integrated into the proposal distribution. Therefore, degree of approximation to the system posterior density is improved. Simulation results show that the estimation error of the ICKPF-MCMC algorithm is much better than other algorithms.
목차
1. Introduction
2. Standard Particle Filter Algorithm (PF)
3. Iterative Cubature Kalman Particle Filter (ICKPF)
4. Iteration Cubature Kalman Particle Filter based on MCMC
4.1. MCMC Moving Steps
4.2. The Iterative Cubature Particle Filter Combined MCMC Algorithm
5. Simulation Results and Analysis
6. Conclusion
Acknowledgements
References