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Novel Gain-Optimized Two-Step Fusion Filtering Method for Ranging-Based Localization Using Predicted Residuals. [PDF]

Sensors (Basel)
Chang B, Zhang X, Sun N, Ni H.
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Research on a Particle Filtering Multi-Target Tracking Algorithm for Distributed Systems. [PDF]

Sensors (Basel)
Han B, Ge Z, Su Z, Hao J.
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Local Linear Wavelet Neural Network-Based Unscented Kalman Filter for Vehicle Collision Estimate Warning System and Ensuring Stable Vehicle-to-Infrastructure Communication. [PDF]

Appl Bionics Biomech
Lema YK, Mishra S, Gelmecha DJ.
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State estimation in an islanded hybrid solar-wind DC microgrid using Unscented Kalman Filter. [PDF]

Heliyon
HajiHeydari Varnoosfaderani N, Khorsandi A.   +1 more
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An Adaptive Spatial Target Tracking Method Based on Unscented Kalman Filter. [PDF]

Sensors (Basel)
Rong D, Wang Y.
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A Novel Variational Bayesian Method with Unknown Noise for Underwater INS/DVL/USBL Localization. [PDF]

Sensors (Basel)
Huang H, Dong C, Zhang Y, Zhang S.
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Optimal LQG controller design for inverted pendulum systems using a comprehensive approach. [PDF]

Sci Rep
Rani M, Kamlu SS.
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GP-UKF: Unscented kalman filters with Gaussian process prediction and observation models

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
This paper considers the use of non-parametric system models for sequential state estimation. In particular, motion and observation models are learned from training examples using Gaussian process (GP) regression. The state estimator is an unscented Kalman filter (UKF). The resulting GP-UKF algorithm has a number of advantages over standard (parametric)
Jonathan Ko, Daniel J. Klein, Dieter Fox, Dirk Haehnel   +3 more
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