ArticleSensors (Basel, Switzerland)2020
An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise.
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 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student's t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student's t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived. In the proposed VBST-CIF, the spherical-radial cubature (SRC) rule is embedded into the variational Bayes (VB) method for a joint estimation of states and scale matrix, degree-of-freedom (DOF) parameter, as well as an auxiliary parameter in the nonlinear system with heavy-tailed noise. The designed VBST-CIF facilitates multi-sensor fusion, allowing one to derive a VBST-CIFF algorithm based on multi-sensor information feedback fusion. The performance of the proposed algorithms is assessed in target tracking scenarios. Simulation results demonstrate that the proposed VBST-CIF/VBST-CIFF outperform the conventional cubature information filter (CIF) and cubature information feedback fusion (CIFF) algorithms.
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Registered trials
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