吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 1831-1837.doi: 10.13229/j.cnki.jdxbgxb201406045

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Quasi-Monte Carlo particle filter algorithm based on sigma point H

KONG Yun-bo1, FENG Xin-xi1, LU Chuan-guo1, LIU Zhen-tao2   

  1. 1. Information and Navigation Institute, Airforce Engineering University, Xi'an 710077,China;
    2. Unit of 95072 of the PLA, Nanning 530000,China
  • Received:2013-03-09 Online:2014-11-01 Published:2014-11-01

Abstract: A new filtering algorithm is proposed. In this algorithm, the probability density function is generated by the sigma point H filter. The sigma point H filter has very high accuracy and strong robustness to uncertain observation noise, and the filter algorithm integrates the new observation. So the generated probability density function can reasonably approximate the real posterior probability distribution of the system state. At the resampling step, the degeneration problem is effectively overcome and the accuracy of state estimation is improved by using the quasi-Monte Carlo-based resampling algorithm. Simulation results demonstrated the feasibility and effectiveness of the proposed algorithm.

Key words: communication, Sigma point transformation, ∞, filtering, quasi-Monte Carlo, particle filtering, nonlinear system

CLC Number: 

  • TN953
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