Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems

Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems PDF Author: Paul D. Abramson
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 354

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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

Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems PDF Author: Paul D. Abramson
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 354

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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

Simultaneous Estimation of the State and Noise Statistics in Linear Dynamical Systems PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 356

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Book Description


Monthly Catalogue, United States Public Documents

Monthly Catalogue, United States Public Documents PDF Author:
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 1250

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Identification of Dynamic Systems

Identification of Dynamic Systems PDF Author: Rolf Isermann
Publisher: Springer
ISBN: 9783540871552
Category : Technology & Engineering
Languages : en
Pages : 705

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Book Description
Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.

Optimal Filtering

Optimal Filtering PDF Author: Brian D. O. Anderson
Publisher: Courier Corporation
ISBN: 0486136892
Category : Science
Languages : en
Pages : 370

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Book Description
Graduate-level text extends studies of signal processing, particularly regarding communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; discrete-time Kalman filter; time-invariant filters; more. 1979 edition.

Bayesian Filtering and Smoothing

Bayesian Filtering and Smoothing PDF Author: Simo Särkkä
Publisher: Cambridge University Press
ISBN: 110703065X
Category : Computers
Languages : en
Pages : 255

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Book Description
A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

National Union Catalog

National Union Catalog PDF Author:
Publisher:
ISBN:
Category : Union catalogs
Languages : en
Pages : 648

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Book Description


Feedback Systems

Feedback Systems PDF Author: Karl Johan Åström
Publisher: Princeton University Press
ISBN: 069121347X
Category : Technology & Engineering
Languages : en
Pages :

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Book Description
The essential introduction to the principles and applications of feedback systems—now fully revised and expanded This textbook covers the mathematics needed to model, analyze, and design feedback systems. Now more user-friendly than ever, this revised and expanded edition of Feedback Systems is a one-volume resource for students and researchers in mathematics and engineering. It has applications across a range of disciplines that utilize feedback in physical, biological, information, and economic systems. Karl Åström and Richard Murray use techniques from physics, computer science, and operations research to introduce control-oriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Åström and Murray then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. Features a new chapter on design principles and tools, illustrating the types of problems that can be solved using feedback Includes a new chapter on fundamental limits and new material on the Routh-Hurwitz criterion and root locus plots Provides exercises at the end of every chapter Comes with an electronic solutions manual An ideal textbook for undergraduate and graduate students Indispensable for researchers seeking a self-contained resource on control theory

The National Union Catalogs, 1963-

The National Union Catalogs, 1963- PDF Author:
Publisher:
ISBN:
Category : American literature
Languages : en
Pages : 652

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Book Description


Introduction to Applied Linear Algebra

Introduction to Applied Linear Algebra PDF Author: Stephen Boyd
Publisher: Cambridge University Press
ISBN: 1316518965
Category : Business & Economics
Languages : en
Pages : 477

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Book Description
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.