Journal ArticleDOI
Robust Kalman filters for linear time-varying systems with stochastic parametric uncertainties
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TLDR
A robust recursive Kalman filtering algorithm that addresses estimation problems that arise in linear time-varying systems with stochastic parametric uncertainties and is shown to converge when the system is mean square stable and the state space matrices are time invariant.Abstract:
We present a robust recursive Kalman filtering algorithm that addresses estimation problems that arise in linear time-varying systems with stochastic parametric uncertainties. The filter has a one-step predictor-corrector structure and minimizes an upper bound of the mean square estimation error at each step, with the minimization reduced to a convex optimization problem based on linear matrix inequalities. The algorithm is shown to converge when the system is mean square stable and the state space matrices are time invariant. A numerical example consisting of equalizer design for a communication channel demonstrates that our algorithm offers considerable improvement in performance when compared with conventional Kalman filtering techniques.read more
Citations
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Book ChapterDOI
Disciplined Convex Programming
TL;DR: A new methodology for constructing convex optimization models called disciplined convex programming is introduced, which enforces a set of conventions upon the models constructed, in turn allowing much of the work required to analyze and solve the models to be automated.
Proceedings ArticleDOI
Segmenting foreground objects from a dynamic textured background via a robust Kalman filter
Jing Zhong,Sclaroff +1 more
TL;DR: This work has developed a novel foreground-background segmentation algorithm that explicitly accounts for the nonstationary nature and clutter-like appearance of many dynamic textures.
Journal ArticleDOI
Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises
TL;DR: A robust finite-horizon Kalman filter is designed for discrete time-varying uncertain systems with both additive and multiplicative noises in terms of two discrete Riccati difference equations.
Book
Global optimization : from theory to implementation
Leo Liberti,Nelson Maculan +1 more
TL;DR: Preface.- Optimization under Composite Monotonic Constraints and Constrained Optimization over the Efficient Set (H. Tuy, N.T. Hoai-Phuong).
Journal ArticleDOI
Recursive filtering with random parameter matrices, multiple fading measurements and correlated noises
TL;DR: The purpose of the addressed filtering problem is to design an unbiased and recursive filter for the random parameter matrices, stochastic nonlinearity, and multiple fading measurements as well as correlated noises.
References
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Book
Applied Optimal Estimation
TL;DR: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation, and the theory and practice of optimal estimation is presented.
Journal Article
Optimal Filtering
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.
Book
Adaptive filtering prediction and control
Graham C. Goodwin,Kwai Sang Sin +1 more
TL;DR: This unified survey focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems and summarizes the theoretical and practical aspects of a large class of adaptive algorithms.