CDC2000 Nonlinear Stochastic Filtering and Estimation

Nonlinear Stochastic Filtering and Estimation
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear 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	Approximate Nonlinear Filtering and Its Applications for GPS Authors: Babak Azimi-Sadjadi, Perinkulam S. Krishnaprasad, Volume: 1, Page 1579 Paper number 2096 Abstract: In this paper we address the problem of nonlinear filtering in the presence of integer uncertainty. This setup is specially important for the case of differential GPS with carrier phase measurements. In simulation results we show that Particle Filtering is capable of resolving integer ambiguity in the given nonlinear setup. Motivated by these results we introduce a new Particle Filtering algorithm that can reduce the computational complexity for a certain class of problems. In this class, it is assumed that the conditional density of the state of the system given the observations is close to a known exponential family of densities. CD002096.PDF (From Author) TOP Stability and Approximation of Nonlinear Filters in the Hilbert Metric, and Application to Particle Filters Authors: François LeGland, Nadia Oudjane, Volume: 1, Page 1585 Paper number 1833 Abstract: In this paper, the stability of the optimal filter w.r.t. its initial condition and w.r.t. the model, is studied in a general HMM using the Hilbert projective metric. These stability results are then used to prove the uniform convergence of the interacting particle filter to the optimal filter, as the number of particles goes to infinity. CD001833.PDF (From Author) TOP Inversion of Nonlinear Stochastic Models for Parameter Estimation Authors: Ola Markusson, Haakan Hjalmarsson, Volume: 1, Page 1591 Paper number 1338 Abstract: Prediction error and maximum likelihood estimation of nonlinear stochastic models requires inversion of the model, a step which may require substantial efforts, either in terms of manual calculations or through the use of software capable of symbolic computations. In this paper we show that model inversion can be easily implemented in numerical software such as, e.g., Simulink and MatrixX, by means of a feedback connection based on the model. We derive sufficient conditions for the existence of a stable causal inverse as well as sufficient conditions for the initial transient to decay. These conditions are given in terms of properties for a linear time-varying system associated with the nonlinear model. The method is illustrated on a numerical example. CD001338.PDF (From Author) TOP Robust Kalman Filter Design for Hybrid Systems with Norm-Bounded Unknown Nonlinearities Authors: Peng Shi, Mehmet Karan, C. Yalcin Kaya, Volume: 1, Page 1597 Paper number 1792 Abstract: This paper considers the filtering problem for a class of linear hybrid systems with nonlinear uncertainties and Markovian jump parameters. The unknown nonlinearities in the system are time-varying and norm-bounded. First, we show the equivalence of the norm bounded linear and nonlinear uncertainty sets. Then, instead of the original hybrid linear system with nonlinear uncertainties, we consider the same system with linear uncertainties. By using a Riccati equation approach for this new system, a robust filter is designed using two sets of coupled Riccati-like equations such that the estimation error is guaranteed to have an upper bound. CD001792.PDF (From Author) CD001792.PDF (Scanned) TOP Efficiency of an Approximate Filter for a Particular Class of Nonlinear Diffusions with Observations Corrupted by Small Noise Authors: Paula Milheiro-Oliveira, Jean Picard, Volume: 1, Page 1599 Paper number 1130 Abstract: The asymptotic behaviour of a nonlinear continuous time approximate filter when the variance of the observation noise tends to 0 is investigated. We consider a particular class of signals modeled by a two-dimensional quasi-linear diffusion from which only one of the components is noisy, and we assume that a one-dimensional linear function of the signal, depending only of the unnoisy component, is observed in a low noise channel. Under some detectability assumptions the unobserved signal can be restored by means of an approximate nonlinear filter. We establish that the filtering error converges to 0 and we give an upper bound for the convergence rate. The efficiency of the approximate filter is compared with the efficiency of the optimal filter and the order of magnitude of the error between the two filters, as the observation noise vanishes, is obtained. A more general case is briefly presented. CD001130.PDF (From Author) TOP