Non Gaussian State Estimation and the Maximum Correntropy Approach

Short Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error in the form of maximum correntropy criterion is examined and the corresponding algorithmic framework is derived.
Seasonal Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error (MMSE) in the form of maximum correntropy criterion (MCC) is examined, the corresponding algorithmic framework derived, and illustrated for various test problems as well as real-life problems. Overall, it explains how MCC is integrated into the conventional Bayesian estimation framework and their implementation to real-life problems. This book is aimed at researchers and graduate students in electrical engineering, robotics, and dynamic systems.
Standard Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error (MMSE) in the form of maximum correntropy criterion (MCC) is examined, the corresponding algorithmic framework derived, and illustrated for various test problems as well as real-life problems. Overall, it explains how MCC is integrated into the conventional Bayesian estimation framework and their implementation to real-life problems.
Reviews well-established non-Gaussian estimation methods including applications of techniques
Covers relaxation of gaussian assumption
Discusses challenges in formulating non-liner non-Gaussian estimation framework
Illustrates the applicability of the algorithms mentioned to real-life problems
Explores derivation of non-linear non-Gaussian estimation framework based on maximum correntropy criterion
This book is aimed at researchers and graduate students in electrical engineering, robotics, and dynamic systems.

1147400017
Non Gaussian State Estimation and the Maximum Correntropy Approach

Short Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error in the form of maximum correntropy criterion is examined and the corresponding algorithmic framework is derived.
Seasonal Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error (MMSE) in the form of maximum correntropy criterion (MCC) is examined, the corresponding algorithmic framework derived, and illustrated for various test problems as well as real-life problems. Overall, it explains how MCC is integrated into the conventional Bayesian estimation framework and their implementation to real-life problems. This book is aimed at researchers and graduate students in electrical engineering, robotics, and dynamic systems.
Standard Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error (MMSE) in the form of maximum correntropy criterion (MCC) is examined, the corresponding algorithmic framework derived, and illustrated for various test problems as well as real-life problems. Overall, it explains how MCC is integrated into the conventional Bayesian estimation framework and their implementation to real-life problems.
Reviews well-established non-Gaussian estimation methods including applications of techniques
Covers relaxation of gaussian assumption
Discusses challenges in formulating non-liner non-Gaussian estimation framework
Illustrates the applicability of the algorithms mentioned to real-life problems
Explores derivation of non-linear non-Gaussian estimation framework based on maximum correntropy criterion
This book is aimed at researchers and graduate students in electrical engineering, robotics, and dynamic systems.

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Non Gaussian State Estimation and the Maximum Correntropy Approach

Non Gaussian State Estimation and the Maximum Correntropy Approach

by Rahul Radhakrishnan, Stepan Ozana
Non Gaussian State Estimation and the Maximum Correntropy Approach
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Non Gaussian State Estimation and the Maximum Correntropy Approach

Non Gaussian State Estimation and the Maximum Correntropy Approach

by Rahul Radhakrishnan, Stepan Ozana

Overview

Short Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error in the form of maximum correntropy criterion is examined and the corresponding algorithmic framework is derived.
Seasonal Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error (MMSE) in the form of maximum correntropy criterion (MCC) is examined, the corresponding algorithmic framework derived, and illustrated for various test problems as well as real-life problems. Overall, it explains how MCC is integrated into the conventional Bayesian estimation framework and their implementation to real-life problems. This book is aimed at researchers and graduate students in electrical engineering, robotics, and dynamic systems.
Standard Blurb
This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. An alternative to the conventional minimum mean square error (MMSE) in the form of maximum correntropy criterion (MCC) is examined, the corresponding algorithmic framework derived, and illustrated for various test problems as well as real-life problems. Overall, it explains how MCC is integrated into the conventional Bayesian estimation framework and their implementation to real-life problems.
Reviews well-established non-Gaussian estimation methods including applications of techniques
Covers relaxation of gaussian assumption
Discusses challenges in formulating non-liner non-Gaussian estimation framework
Illustrates the applicability of the algorithms mentioned to real-life problems
Explores derivation of non-linear non-Gaussian estimation framework based on maximum correntropy criterion
This book is aimed at researchers and graduate students in electrical engineering, robotics, and dynamic systems.

Product Details

ISBN-13: 9781040435915
Publisher: CRC Press
Publication date: 11/30/2025
Series: Control Theory and Applications
Sold by: Barnes & Noble
Format: eBook
Pages: 208

About the Author

Rahul Radhakrishnan received the B.Tech. degree in Applied Electronics and Instrumentation from the Government Engineering College, Calicut, India, in 2010 and the M.Tech. degree in Control Systems from the Department of Electrical Engineering, National Institute of Technology Kurukshetra, India, in 2013. He received the Ph.D. degree from the Department of Electrical Engineering, Indian Institute of Technology Patna, India, in 2018. Currently, he is working as an Assistant Professor in the Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India. His main research interests include nonlinear filtering, aerospace, underwater target tracking, state-of-charge estimation, and process control.

Table of Contents

1. Introduction 2. Estimation With Weighted Least Squares 3. Recursive State Estimation: Linear Systems 4. Nonlinear State Estimation 5. Maximum Correntropy Algorithms For Nonlinear Systems 6. Maximum Correntropy Algorithms For Non-Gaussian Systems 7. Angles-Only Target Tracking 8. Tracking And Interception Of Ballistic Target On Re-entry 9. Application To Process Control: Quadruple Tank System
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