A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Clicks: 277
ID: 267122
2020
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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.
Reference Key
bai2020sensorsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yu-ting Bai;Xiao-yi Wang;Xue-bo Jin;Zhi-yao Zhao;Bai-hai Zhang;Bai, Yu-ting;Wang, Xiao-yi;Jin, Xue-bo;Zhao, Zhi-yao;Zhang, Bai-hai;
Journal sensors
Year 2020
DOI
10.3390/s20010299
URL
https://www.mdpi.com/1424-8220/20/1/299
Keywords
Neural network noise filtering kalman filter nonlinear autoregressive

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