ArticleSensors (Basel, Switzerland)2020
A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Adaptive Multi-Sensor Joint Tracking Algorithm with Unknown Noise Characteristics.Sensors (Basel, Switzerland) · 2024Article
- Robust Fusion Kalman Estimator of the Multi-Sensor Descriptor System with Multiple Types of Noises and Packet Loss.Sensors (Basel, Switzerland) · 2023Article
- Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series.Entropy (Basel, Switzerland) · 2022Article
- Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification.Sensors (Basel, Switzerland) · 2022Article
- Crack Size Identification for Bearings Using an Adaptive Digital Twin.Sensors (Basel, Switzerland) · 2021Article
- A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction.Entropy (Basel, Switzerland) · 2021Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
- Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs.Sensors (Basel, Switzerland) · 2020Article
- Robust Visual Tracking Based on Adaptive Multi-Feature Fusion Using the Tracking Reliability Criterion.Sensors (Basel, Switzerland) · 2020Article
- An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise.Sensors (Basel, Switzerland) · 2020Article
- Sensor Signal and Information Processing III.Sensors (Basel, Switzerland) · 2020Article
- An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection.International journal of environmental research and public health · 2020Article
- Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
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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.
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