吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (04): 1139-1145.doi: 10.7964/jdxbgxb201304047

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Adaptive unscented particle filter algorithm under unknown noise

LI Yu-chen1,2, LI Zhan-ming1   

  1. 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050,China;
    2. Key Laboratory of Advanced Control of Industrial Processes of Gansu Province, Lanzhou 730050,China
  • Received:2012-04-11 Online:2013-07-01 Published:2013-07-01

Abstract:

In order to solve the target tracking problem when the statistic characteristics of the system are unknown, an adaptive unscented particle filter algorithm is proposed. This algorithm estimates and corrects the statistic characteristics of the unknown system noise in real-time using improved Sage-Husa estimator. Combining with unscented Kalman filter, the algorithm produces the optimal proposal distribution function. This method effectively reduces the estimation error and improves the anti-noise ability of the system. Theoretical analysis and experiments show that the new method can significantly improve the accuracy and stability of target tracking when the statistic characteristics of the system are unknown.

Key words: information processing, particle filter, adaptive filtering, unscentde Kalman filter, target tracking

CLC Number: 

  • TN911

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