Data Processing with an Innovation Self-Adaptive Denoising Amalgamation Algorithm

Jian Yu Hu; Shu Ming Hou; Yan Fei Liu

doi:10.4028/www.scientific.net/AMR.1003.254

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1003Data Processing with an Innovation Self-Adaptive...

Data Processing with an Innovation Self-Adaptive Denoising Amalgamation Algorithm

Abstract:

The process noise and observation noise of a system are easily disturbed. It’s hard to know its statistic character. This paper proposes an innovation self-adaptive fading UPF algorithm to solve this problem. In the new algorithm, self-adaptive gradually vanishing UKF is used as weightiness density function of particle filter. New observation data is used to modify the error caused by state function of system and noise statistic parameter in time. What’s more, the new algorithm avoids traditional particle filter’s defect that it always gets part optimal solutions. Experiment results indicate that this new algorithm has a higher accuracy and robustness for the changeable noise statistics and non-linear system.

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Periodical:

Advanced Materials Research (Volume 1003)

Pages:

254-259

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1003.254

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Online since:

July 2014

Authors:

Jian Yu Hu*, Shu Ming Hou, Yan Fei Liu

Keywords:

Innovation, INS, Particle Filter, Self-Adaptive, UKF

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