CDC2000 Filtering in Continuous Time Stochastic Systems

Filtering in Continuous Time Stochastic Systems
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Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Filtering for Linear Systems Driven by Fractional Brownian Motion Authors: Nasir U. Ahmed, Charalambos D. Charalambous, Volume: 1, Page 4259 Paper number 1742 Abstract: In this paper we study continuous time filtering for linear systems driven by fractional Brownian motion processes. We present the derivation of the optimum linear filter equations which involve a pair of functional-differential equations giving the error co-variance (matrix-valued) function and the filter. These equations are the appropriate substitutes of the matrix-Riccati differential equation arising in classical Kalman filtering. However the optimum filter has the classical appearance and, as usual, it is driven by the increments of the observed process. Our derivation is based on the same general principles as used in [5,6,7]. CD001742.PDF (From Author) TOP A New Suboptimal Approach To The Filtering Problem For Bilinear Stochastic Differential Systems Authors: Francesco Carravetta, Alfredo Germani, Marat K. Shuakayev, Volume: 1, Page 4264 Paper number 3 Abstract: The aim of this paper is to present a new approach to the filtering problem for the class of bilinear stochastic multivariable systems, consisting in searching for suboptimal state-estimates instead of the conditional statistics. As a first result, a finite-dimensional optimal linear filter for the considered class of systems is defined. Then, the more general problem of designing polynomial finite-dimensional filters is considered. The equations of a finite-dimensional filter are given, producing a state-estimate which is optimal in a class of polynomial transformations of the measurements with arbitrarily fixed degree. Numerical simulations show the effectiveness of the proposed filter CD000003.PDF (From Author) TOP Filtering of Nonlinear Stochastic Feedback Systems Authors: Francesco Carravetta, Alfredo Germani, Robert S. Liptser, Costanzo Manes, Volume: 1, Page 4270 Paper number 80 Abstract: This paper concerns the filtering problem for the class of stochastic nonlinear systems on which an output feedback can be closed. It is proven that the optimal filter for the open-loop system remains optimal when the feedback is closed. CD000080.PDF (From Author) TOP On Pathwise Uniqueness for the Zakai Filter Equation Authors: Vladimir M. Lucic, Andrew J. Heunis, Volume: 1, Page 4274 Paper number 1982 Abstract: We study a nonlinear filtering problem in which the signal to be estimated is conditioned by the observations. The main result establishes pathwise uniqueness for the unnormalized (Zakai) filter equation. CD001982.PDF (From Author) TOP Risk Sensitive Filtering Equations In Infinite Dimensional Spaces With Counting Observation Authors: Patrick A. Florchinger, Volume: 1, Page 4280 Paper number 1081 Abstract: The purpose of this paper is to compute the risk-sensitive filtering equations when the state process, given as the solution of a stochastic differential equation on an infinite dimensional Hilbert space, is observed through a counting observation. CD001081.PDF (From Author) TOP Existence of a Density for the Filter Associated to Hilbert-Space Valued Systems Authors: Marie-Noelle C. Dietsch, Volume: 1, Page 4282 Paper number 1082 Abstract: The purpose of this paper is to study a correlated filtering problem, when both the signal and the observation processes evolve in infinite dimensional Hilbert spaces H and V. Our aim is to prove that the filter associated to this filtering problem, which solves a parabolic stochastic partial differential equation, the Zakai equation, is absolutely continuous with respect to a given reference measure on H. In fact, we prove that the Zakai equation solved by the unnormalized filter has a solution which admits a density with respect to the given measure and we compute the equation solved by this density. CD001082.PDF (From Author) TOP Some Results In Abstract Optimal Linear Filtering Authors: Vladimir Fomin, Michael Ruzhansky, Volume: 1, Page 4284 Paper number 16 Abstract: The linear optimal filtering problems in infinite dimensional Hilbert spaces and their extensions are discussed. The quality functional is allowed to be a general quadratic functional defined by a possibly degenerate operator. We describe the solution of the stable and the causal filtering problems. In the case of causal filtering, we establish the relation with a relaxed causal filtering problem in the extended space. We solve the last problem in continuous and discrete cases and give the necessary and sufficient conditions for the solvability of the original causal problem as well as the conditions for the analogue of Bode-Shannon formula to define an optimal filter. CD000016.PDF (From Author) TOP