Non Gaussian State Estimation and the Maximum Correntropy Approach

Rahul Radhakrishnan, Stepan Ozana

ISBN 10: 1032581972 / ISBN 13: 9781032581972

Published by Taylor and Francis Ltd, GB, 2025

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. Seller Inventory # LU-9781032581972

Synopsis:

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.

About the Author:

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

Bibliographic Details

Title: Non Gaussian State Estimation and the ...
Publisher: Taylor and Francis Ltd, GB
Publication Date: 2025
Binding: Hardback
Condition: New