About: State vector is a(n) research topic. Over the lifetime, 5479 publication(s) have been published within this topic receiving 103755 citation(s).
01 Apr 1993-
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Abstract: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.
24 Mar 2004-
Abstract: Preface INTRODUCTION Operating States of a Power System Power System Security Analysis State Estimation Summary WEIGHTED LEAST SQUARES STATE ESTIMATION Introduction Component Modeling and Assumptions Building the Network Model Maximum Likelihood Estimation Measurement Model and Assumptions WLS State Estimation Algorithm Decoupled Formulation of the WLS State Estimation DC State Estimation Model Problems References ALTERNATIVE FORMULATIONS OF THE WLS STATE ESTIMATION Weaknesses of the Normal Equations Formulation Orthogonal Factorization Hybrid Method Method of Peters and Wilkinson Equality-Constrained WLS State Estimation Augmented Matrix Approach Blocked Formulation Comparison of Techniques Problems References NETWORK OBSERVABILITY ANALYSIS Networks and Graphs NetworkMatrices LoopEquations Methods of Observability Analysis Numerical Method Based on the Branch Variable Formulation Numerical Method Based on the Nodal Variable Formulation Topological Observability Analysis Method Determination of Critical Measurements Measurement Design Summary Problems References BAD DATA DETECTION AND IDENTIFICATION Properties of Measurement Residuals Classification of Measurements Bad Data Detection and IdentiRability Bad Data Detection Properties of Normalized Residuals Bad Data Identification Largest Normalized Residual Test Hypothesis Testing Identification (HTI) Summary Problems References ROBUST STATE ESTIMATION Introduction Robustness and Breakdown Points Outliers and Leverage Points M-Estimators Least Absolute Value (LAV) Estimation Discussion Problems References NETWORK PARAMETER ESTIMATION Introduction Influence of Parameter Errors on State Estimation Results Identification of Suspicious Parameters Classification of Parameter Estimation Methods Parameter Estimation Based on Residua! Sensitivity Analysis Parameter Estimation Based on State Vector Augmentation Parameter Estimation Based on Historical Series of Data Transformer Tap Estimation Observability of Network Parameters Discussion Problems References TOPOLOGY ERROR PROCESSING Introduction Types of Topology Errors Detection of Topology Errors Classification of Methods for Topology Error Analysis Preliminary Topology Validation Branch Status Errors Substation Configuration Errors Substation Graph and Reduced Model Implicit Substation Model: State and Status Estimation Observability Analysis Revisited Problems References STATE ESTIMATION USING AMPERE MEASUREMENTS Introduction Modeling of Ampere Measurements Difficulties in Using Ampere Measurements Inequality-Constrained State Estimation Heuristic Determination of F-# Solution Uniqueness Algorithmic Determination of Solution Uniqueness Identification of Nonuniquely Observable Branches Measurement Classification and Bad Data Identification Problems References Appendix A Review of Basic Statistics Appendix B Review of Sparse Linear Equation Solution References Index
01 Sep 1994-Biometrika
TL;DR: This work shows how to use the Gibbs sampler to carry out Bayesian inference on a linear state space model with errors that are a mixture of normals and coefficients that can switch over time.
Abstract: SUMMARY We show how to use the Gibbs sampler to carry out Bayesian inference on a linear state space model with errors that are a mixture of normals and coefficients that can switch over time. Our approach simultaneously generates the whole of the state vector given the mixture and coefficient indicator variables and simultaneously generates all the indicator variables conditional on the state vectors. The states are generated efficiently using the Kalman filter. We illustrate our approach by several examples and empirically compare its performance to another Gibbs sampler where the states are generated one at a time. The empirical results suggest that our approach is both practical to implement and dominates the Gibbs sampler that generates the states one at a time.
01 Apr 1966-IEEE Transactions on Automatic Control
Abstract: Often in control design it is necessary to construct estimates of state variables which are not available by direct measurement. If a system is linear, its state vector can be approximately reconstructed by building an observer which is itself a linear system driven by the available outputs and inputs of the original system. The state vector of an n th order system with m independent outputs can be reconstructed with an observer of order n-m . In this paper it is shown that the design of an observer for a system with M outputs can be reduced to the design of m separate observers for single-output subsystems. This result is a consequence of a special canonical form developed in the paper for multiple-output systems. In the special case of reconstruction of a single linear functional of the unknown state vector, it is shown that a great reduction in observer complexity is often possible. Finally, the application of observers to control design is investigated. It is shown that an observer's estimate of the system state vector can be used in place of the actual state vector in linear or nonlinear feedback designs without loss of stability.
01 Apr 1964-IEEE Transactions on Military Electronics
Abstract: In much of modern control theory designs are based on the assumption that the state vector of the system to be controlled is available for measurement. In many practical situations only a few output quantities are available. Application of theories which assume that the state vector is known is severely limited in these cases. In this paper it is shown that the state vector of a linear system can be reconstructed from observations of the system inputs and outputs. It is shown that the observer, which reconstructs the state vector, is itself a linear system whose complexity decreases as the number of output quantities available increases. The observer may be incorporated in the control of a system which does not have its state vector available for measurement. The observer supplies the state vector, but at the expense of adding poles to the over-all system.