Author: Paul D. Abramson
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 354
Book Description
An optimal procedure for estimating the state of a linear dynamical system when the statistics of the measurement and process noise are poorly known is developed. The criterion of maximum likelihood is used to obtain an optimal estimate of the state and noise statistics. These estimates are shown to be asymptotically unbiased, efficient, and unique, with the estimation error normally distributed with a known covariance. The resulting equations for the estimates cannot be solved recursively, but an iterative procedure for their solution is presented. Several approximate solutions are presented which reduce the necessary computations in finding the estimates. Some of the approximate solutions allow a real time estimation of the state and noise statistics. Closely related to the estimation problem is the subject of hypothesis testing. Several criteria are developed for testing hypotheses concerning the values of the noise statistics that are used in the computation of the appropriate filter gains in a linear Kalman type state estimator. If the observed measurements are not consistent with the assumptions about the noise statistics, then estimation of the noise statistics should be undertaken using either optimal or suboptimal procedures. Numerical results of a digital computer simulation of the optimal and suboptimal solutions of the estimation problem are presented for a simple but realistic example.
Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems
Author: Paul D. Abramson
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 354
Book Description
An optimal procedure for estimating the state of a linear dynamical system when the statistics of the measurement and process noise are poorly known is developed. The criterion of maximum likelihood is used to obtain an optimal estimate of the state and noise statistics. These estimates are shown to be asymptotically unbiased, efficient, and unique, with the estimation error normally distributed with a known covariance. The resulting equations for the estimates cannot be solved recursively, but an iterative procedure for their solution is presented. Several approximate solutions are presented which reduce the necessary computations in finding the estimates. Some of the approximate solutions allow a real time estimation of the state and noise statistics. Closely related to the estimation problem is the subject of hypothesis testing. Several criteria are developed for testing hypotheses concerning the values of the noise statistics that are used in the computation of the appropriate filter gains in a linear Kalman type state estimator. If the observed measurements are not consistent with the assumptions about the noise statistics, then estimation of the noise statistics should be undertaken using either optimal or suboptimal procedures. Numerical results of a digital computer simulation of the optimal and suboptimal solutions of the estimation problem are presented for a simple but realistic example.
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 354
Book Description
An optimal procedure for estimating the state of a linear dynamical system when the statistics of the measurement and process noise are poorly known is developed. The criterion of maximum likelihood is used to obtain an optimal estimate of the state and noise statistics. These estimates are shown to be asymptotically unbiased, efficient, and unique, with the estimation error normally distributed with a known covariance. The resulting equations for the estimates cannot be solved recursively, but an iterative procedure for their solution is presented. Several approximate solutions are presented which reduce the necessary computations in finding the estimates. Some of the approximate solutions allow a real time estimation of the state and noise statistics. Closely related to the estimation problem is the subject of hypothesis testing. Several criteria are developed for testing hypotheses concerning the values of the noise statistics that are used in the computation of the appropriate filter gains in a linear Kalman type state estimator. If the observed measurements are not consistent with the assumptions about the noise statistics, then estimation of the noise statistics should be undertaken using either optimal or suboptimal procedures. Numerical results of a digital computer simulation of the optimal and suboptimal solutions of the estimation problem are presented for a simple but realistic example.
Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems
Author: Paul Dowling Abramson (Jr)
Publisher:
ISBN:
Category :
Languages : en
Pages : 342
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 342
Book Description
Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems
Author: Paul Dowling Abramson (Jr)
Publisher:
ISBN:
Category :
Languages : en
Pages : 342
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 342
Book Description
Simultaneous Estimation of State and Noise Statistics of Linear Dynamical Systems [with List of References]
Author:
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 342
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 342
Book Description
Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 356
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 356
Book Description
NASA Technical Report
Author:
Publisher:
ISBN:
Category : Aerodynamics
Languages : en
Pages : 1096
Book Description
Publisher:
ISBN:
Category : Aerodynamics
Languages : en
Pages : 1096
Book Description
Scientific and Technical Aerospace Reports
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 1368
Book Description
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 1368
Book Description
Monthly Catalogue, United States Public Documents
Author:
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 1250
Book Description
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 1250
Book Description
Applied Mechanics Reviews
Author:
Publisher:
ISBN:
Category : Mechanics, Applied
Languages : en
Pages : 568
Book Description
Publisher:
ISBN:
Category : Mechanics, Applied
Languages : en
Pages : 568
Book Description
Optimal Estimation of Dynamic Systems
Author: John L. Crassidis
Publisher: CRC Press
ISBN: 0203509129
Category : Mathematics
Languages : en
Pages : 606
Book Description
Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals receiv
Publisher: CRC Press
ISBN: 0203509129
Category : Mathematics
Languages : en
Pages : 606
Book Description
Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals receiv