Cubature Kalman Filtering Under Information Theoretic Criteria

Badong Chen, Lujuan Dang, Nanning Zheng and Jose C. Principe
Additional contact information
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 7 in Kalman Filtering Under Information Theoretic Criteria, 2023, pp 191-228 from Springer

Abstract: Abstract In unscented Kalman filter (UKF), the choice of scaling parameter in UKF is still a challenge in practice. To ease the burden on the selection of the scaling parameter, the cubature Kalman filter (CKF) has been introduced, which is based on the spherical–radial cubature rule for numerical integration. For a nonlinear system, the CKF can obtain good performance in Gaussian noises. By utilizing the maximum correntropy criterion (MCC) to improve the robust performance, the CKF and its square-root version based on MCC are provided in this chapter. The two algorithms are named as the maximum correntropy cubature Kalman filter (MCCKF) and maximum correntropy square-root cubature Kalman filter (MCSCKF). The new filters not only retain the advantages of CKF, but also exhibit robust performance against heavy-tailed non-Gaussian noises. However, when the dynamic system suffers from complex non-Gaussian disturbances, the estimates obtained by MCCKF may be obviously biased. To address this issue, the cubature Kalman filter under minimum error entropy with fiducial points (MEEF-CKF) is presented to improve the robustness against noises. The MEEF-CKF can achieve high estimation accuracy and strong robustness in the complex non-Gaussian noises especially in multimodal distribution noise. In this chapter, the detailed derivation about these methods is presented.

Keywords: Cubature Kalman filter; Maximum correntropy cubature Kalman filter; Maximum correntropy square-root cubature Kalman filter; Cubature Kalman filter under minimum error entropy with fiducial points (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-33764-2_7

Ordering information: This item can be ordered from
http://www.springer.com/9783031337642

DOI: 10.1007/978-3-031-33764-2_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().