Introduction

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 1 in Kalman Filtering Under Information Theoretic Criteria, 2023, pp 1-9 from Springer

Abstract: Abstract Kalman filtering is a powerful technology for state estimation of a dynamic system, which finds applications in many areas, including navigation, guidance, data integration, pattern recognition, tracking, and control systems. Kalman filtering yields an optimal estimator when the system is linear and innovation and noise are Gaussian. The Gaussian assumption is, however, seldom the case in real-world applications, where noise distributions tend to be skewed, multimodal, or heavy-tailed. This will create problems for Kalman filtering because the solution is no longer the optimal and state estimation performance may degrade seriously in the case of non-Gaussian noise. To solve the performance degradation in non-Gaussian noises, robust Kalman filtering including three categories is reviewed. Especially, Kalman filtering under information theoretic criteria can achieve excellent performance in complicated non-Gaussian noises with reasonable computation, yielding great practical application potential. In this chapter, the detailed introduction about these methods is presented.

Keywords: Kalman filtering; Robust Kalman filtering; Information theoretic criteria; Non-Gaussian noise (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-33764-2_1

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