Earticle

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

Abstract
 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

키워드

저자

• Song Gao [ School of Electronic Information Engineering Xi'an Technological University, Xi'an 710021, China ]
• Yenan Liu [ School of Electronic Information Engineering Xi'an Technological University, Xi'an 710021, China ]
• Chaobo Chen [ School of Electronic Information Engineering Xi'an Technological University, Xi'an 710021, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605