ArticleSensors (Basel, Switzerland)2020
Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty.
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 9 papers.
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9 citing papers in PubMed.
- AGV navigation in complex environments: A cooperative trajectory optimization strategy based on SMF-MPC.PloS one · 2026Article
- Fuzzy Logic-Based Adaptive Filtering for Transfer Alignment.Sensors (Basel, Switzerland) · 2025Article
- A Kalman Filter-Based Localization Calibration Method Optimized by Reinforcement Learning and Information Matrix Fusion.Entropy (Basel, Switzerland) · 2025Article
- An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems.Sensors (Basel, Switzerland) · 2025Article
- An advanced error state Kalman filter (ESKF)-based terrain contour matching (TERCOM) method for tracking an aerial vehicle using a low-cost digital elevation map.PeerJ. Computer science · 2025Article
- Ego-Vehicle Speed Correction for Automotive Radar Systems Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- A Study on Predicting the Deviation of Jet Trajectory Falling Point under the Influence of Random Wind.Sensors (Basel, Switzerland) · 2024Article
- A real-time prediction interval correction method with an unscented Kalman filter for settlement monitoring of a power station dam.Scientific reports · 2023Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
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4 authors.
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Abstract
This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysis with EKF. It is shown that the SM-HKF outperforms EKF for nonlinear state estimation with systematic UBB error and stochastic error.
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