PDF [DOWNLOAD] Non Gaussian State Estimation

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

• Non Gaussian State Estimation and the Maximum Correntropy Approach
• Rahul Radhakrishnan, Stepan Ozana
• Page: 216
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781040435915
• Publisher: CRC Press

Non Gaussian State Estimation and the Maximum Correntropy Approach

Download ebooks in txt format free Non Gaussian State Estimation and the Maximum Correntropy Approach English version 9781040435915 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.

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