Titlebook: Kalman Filtering Under Information Theoretic Criteria; Badong Chen,Lujuan Dang,Jose C. Principe Book 2023 The Editor(s) (if applicable) an

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Information Theoretic Criteria,apability of model prediction when facing more complex non-Gaussian noises, such as noises from multimodal distributions. Sometimes, in order to obtain an optimal solution, the MEE needs to manually add a bias to the model to yield zero mean error. To more naturally adjust the error mean, the MEE wi

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Kalman Filtering Under Information Theoretic Criteria, maximum correntropy criterion (GMCKF) is also derived. The GMCKF is more general and flexible, which includes the MCKF with Gaussian kernel as a special case. In addition, to better deal with more complicated non-Gaussian noises such as noises from multimodal distributions, the minimum error entrop

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Cubature Kalman Filtering Under Information Theoretic Criteria,ssian 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 stro

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Introduction,ance, 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

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Kalman Filtering, robotics, with an enormous importance in the industry. The actual applications include parameter estimation, system identification, target tracking, simultaneous localization, and many others. The purpose of this chapter is to briefly review the foundations of statistical estimation. For linear dyn