An improved particle filtering algorithm based on observation inversion optimal sampling |
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Authors: | Zhen-tao Hu Quan Pan Feng Yang and Yong-mei Cheng |
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Affiliation: | College of Automation, Northwestern Polytechnical University, Xi'an 710072, China |
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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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Keywords: | particle filter proposal distribution re-sampling observation inversion |
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