Extended Kalman Filtering Under Information Theoretic Criteria

Badong Chen, Lujuan Dang, Nanning Zheng and Jose C. Principe
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Badong Chen: National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, National Key Laboratory of Human-Machine Hybrid Augmented Intelligence
Lujuan Dang: National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, National Key Laboratory of Human-Machine Hybrid Augmented Intelligence
Nanning Zheng: National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, National Key Laboratory of Human-Machine Hybrid Augmented Intelligence
Jose C. Principe: University of Florida, Electrical and Computer Engineering Department

Chapter Chapter 5 in Kalman Filtering Under Information Theoretic Criteria, 2023, pp 127-148 from Springer

Abstract: Abstract To improve the performance in nonlinear situations, the extended Kalman filter (EKF) is broadly applied in many areas, particularly in nonlinear target tracking. Compared to unscented Kalman filter (UKF) and cubature Kalman filter (CKF), the main advantages of EKF are its conceptual and computational simplicity. The maximum correntropy criterion (MCC) and minimum error entropy (MEE) can be applied to EKF to improve the robustness to non-Gaussian noises, resulting in two alternatives: the maximum correntropy extended Kalman filter (MCEKF) and minimum error entropy extended Kalman filter (MEE-EKF). In this chapter, the detailed derivation about these methods is presented.

Keywords: Extended Kalman filter; Maximum correntropy extended Kalman filter; Minimum error entropy extended Kalman filter (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-33764-2_5

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