Applied Optimal Estimation

Author: Analytic Sciences Corporation. Technical Staff
Publisher: MIT Press
ISBN: 9780262570480
Format: PDF
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This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation. Even so, theoretical and mathematical concepts are introduced and developed sufficiently to make the book a self-contained source of instruction for readers without prior knowledge of the basic principles of the field. The work is the product of the technical staff of The Analytic Sciences Corporation (TASC), an organization whose success has resulted largely from its applications of optimal estimation techniques to a wide variety of real situations involving large-scale systems. Arthur Gelb writes in the Foreword that "It is our intent throughout to provide a simple and interesting picture of the central issues underlying modern estimation theory and practice. Heuristic, rather than theoretically elegant, arguments are used extensively, with emphasis on physical insights and key questions of practical importance." Numerous illustrative examples, many based on actual applications, have been interspersed throughout the text to lead the student to a concrete understanding of the theoretical material. The inclusion of problems with "built-in" answers at the end of each of the nine chapters further enhances the self-study potential of the text. After a brief historical prelude, the book introduces the mathematics underlying random process theory and state-space characterization of linear dynamic systems. The theory and practice of optimal estimation is them presented, including filtering, smoothing, and prediction. Both linear and non-linear systems, and continuous- and discrete-time cases, are covered in considerable detail. New results are described concerning the application of covariance analysis to non-linear systems and the connection between observers and optimal estimators. The final chapters treat such practical and often pivotal issues as suboptimal structure, and computer loading considerations. This book is an outgrowth of a course given by TASC at a number of US Government facilities. Virtually all of the members of the TASC technical staff have, at one time and in one way or another, contributed to the material contained in the work.

Optimal Estimation of Dynamic Systems Second Edition

Author: John L. Crassidis
Publisher: CRC Press
ISBN: 1439839867
Format: PDF, ePub, Docs
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Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB® codes for the examples are available on the book’s website. New to the Second Edition With more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises. An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking.

Kalman Filter F r Einsteiger

Author: Phil Kim
Publisher: Createspace Independent Publishing Platform
ISBN: 9781502723789
Format: PDF, Docs
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Das Kalman-Filter ist eine Wunderwaffe, wenn es darum geht digitale Signale in Echtzeit vom Rauschen zu befreien,nicht messbare Signale zu sch�tzen, Objekte zu tracken, Daten zu fusionieren und Messaussetzer zu �berbr�cken.Es findet Anwendung in Robotern, Raketen, Automobilen und selbst in M�hrobotern und autonomen Staubsaugern. H�ufig sind Ingenieurinnen und Ingenieure jedoch von der umfassenden Mathematik abgeschreckt. Das Buch "Kalman-Filter f�r Einsteiger" w�hlt einen unkonventionellen Einstieg in diese aktuelle Filtertechnik.Es beschreibt pragmatisch anhand von zahlreichen MATLAB(R)-Beispielen die Grundlagen des Filters und lehrt die Auslegung ohnemathematische Herleitungen. Dieses Buch erleichtert den Einstieg in die Signalverarbeitung und macht Sie in kurzer Zeit zu Kalman-Filter-Anwendungsprofis.

Optimal Control

Author: Frank L. Lewis
Publisher: John Wiley & Sons
ISBN: 1118122720
Format: PDF, ePub, Mobi
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A NEW EDITION OF THE CLASSIC TEXT ON OPTIMAL CONTROL THEORY As a superb introductory text and an indispensable reference, this new edition of Optimal Control will serve the needs of both the professional engineer and the advanced student in mechanical, electrical, and aerospace engineering. Its coverage encompasses all the fundamental topics as well as the major changes that have occurred in recent years. An abundance of computer simulations using MATLAB and relevant Toolboxes is included to give the reader the actual experience of applying the theory to real-world situations. Major topics covered include: Static Optimization Optimal Control of Discrete-Time Systems Optimal Control of Continuous-Time Systems The Tracking Problem and Other LQR Extensions Final-Time-Free and Constrained Input Control Dynamic Programming Optimal Control for Polynomial Systems Output Feedback and Structured Control Robustness and Multivariable Frequency-Domain Techniques Differential Games Reinforcement Learning and Optimal Adaptive Control