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An improved particle filtering algorithm based on observation inversion optimal sampling

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Abstract

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter, an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly, virtual observations were generated from the latest observation, and two sampling strategies were presented. Then, the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally, the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method, sampling particles can make full use of the latest observation information and the priori modeling information, so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter, the extended Kalman particle filter and the unscented particle filter.

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Authors and Affiliations

  1. College of Automation, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhen-tao Hu  (胡振涛), Quan Pan  (潘 泉), Feng Yang  (杨 峰) & Yong-mei Cheng  (程咏梅)

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  1. Zhen-tao Hu  (胡振涛)
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  2. Quan Pan  (潘 泉)
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  3. Feng Yang  (杨 峰)
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  4. Yong-mei Cheng  (程咏梅)
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Corresponding author

Correspondence to Zhen-tao Hu  (胡振涛).

Additional information

Foundation item: Project(60634030) supported by the Key Project of the National Natural Science Foundation of China; Project(60702066) supported by the National Natural Science Foundation of China; Project (2007ZC53037) supported by Aviation Science Foundation of China; Project(CASC0214) supported by the Space-Flight Innovation Foundation of China

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Hu, Zt., Pan, Q., Yang, F. et al. An improved particle filtering algorithm based on observation inversion optimal sampling. J. Cent. South Univ. Technol. 16, 815–820 (2009). https://doi.org/10.1007/s11771-009-0135-y

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  • DOI: https://doi.org/10.1007/s11771-009-0135-y

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