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- Title
A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.
- Authors
Bai, Yu-ting; Wang, Xiao-yi; Jin, Xue-bo; Zhao, Zhi-yao; Zhang, Bai-hai
- Abstract
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.
- Subjects
KALMAN filtering; AUTOREGRESSIVE models; PARAMETER estimation; ADAPTIVE filters; SOFT computing
- Publication
Sensors (14248220), 2020, Vol 20, Issue 1, p299
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s20010299