Non Gaussian State Estimation and the Maximum Correntropy Approach

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Inbunden, Engelska, 2025

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This monograph aims to present the recent advances in state estimation, in terms of relaxing the conventional assumption that probability densities remain Gaussian. The book explains how MCC is integrated into the conventional Bayesian estimation framework and their implementation to real-life problems. Features: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.

Produktinformation

Utgivningsdatum2025-12-01
Mått156 x 234 x 16 mm
Vikt560 g
FormatInbunden
SpråkEngelska
SerieControl Theory and Applications
Antal sidor197
FörlagTaylor & Francis Ltd
ISBN9781032581972

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Rahul Radhakrishnan was born in Kerala, India in December 1988. He studied Applied Electronics and Instrumentation at the Government Engineering College, Kozhikode, and did M.Tech in Control Systems at National Institute of Technology Kurukshetra. He received the Ph.D. degree in nonlinear filtering and its applications to target tracking problems from the Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, India, in 2018. Before joining as an Assistant Professor with the Department of Electrical Engineering, SVNIT Surat, India, he worked as a post-doctoral fellow in the Department of Chemical Engineering, Indian Institute of Technology Bombay. Presently, he is working as an Assistant Professor in the Department of Electrical Engineering, National Institute of Technology Calicut, India. His main research interest includes nonlinear filtering, aerospace and under□water target tracking, moving horizon estimation, estimation of remaining useful life in energy storage systems, and process control.Stepan Ozana was born in Bilovec, Czech Republic, in May 1977. He studied electrical engineering at the VSB Technical University of Ostrava, and received the M.Sc. degree in control and measurement engineering, in 2000, and the Ph.D. degree in technical cybernetics, in 2004. In 2015, he was habilitated in technical cybernet□ics. Since then, he has been working as an Associate Professor with the Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava. He currently gives lec□tures on cybernetics and control systems. His main areas of interest and expertise are modeling and simulation of dynamic systems, control theory, automation, design, implementation, and deployment of control algorithms using soft PLC systems

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