ArticleSensors (Basel, Switzerland)2022
Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Prediction-Enhanced Polynomial Kalman Filter Using Higher-Order Predictive Representations for State Estimation.Sensors (Basel, Switzerland) · 2026Article
- Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization.Heliyon · 2024Article
- A High-Order Kalman Filter Method for Fusion Estimation of Motion Trajectories of Multi-Robot Formation.Sensors (Basel, Switzerland) · 2022Article
- A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning.Sensors (Basel, Switzerland) · 2022Article
- Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF).Sensors (Basel, Switzerland) · 2022Article
- A Novel Data Sampling Driven Kalman Filter Is Designed by Combining the Characteristic Sampling of UKF and the Random Sampling of EnKF.Sensors (Basel, Switzerland) · 2022Article
- Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform.Sensors (Basel, Switzerland) · 2022Article
- A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented system errors are illustratively charactered by Gaussian white noise. Thirdly, at the expanded space a creative high-order Kalman filter is delicately designed, which consists of high-order Taylor expansion, introducing magical intermediate variables, representing linear systems converted from strongly nonlinear systems, designing Kalman filter, etc. The performance of the proposed filter will be much better than one of EKF, because it uses more information than EKF. Finally, its promise is verified through commonly used digital simulation examples.
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