Control Theory and Applications

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.