Dynamic data processing (Record no. 39853)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03726nam a2200385 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | OTLid0001727 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MnU |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20241120064038.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION | |
| fixed length control field | m o d s |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 241011s2024 mnu o 0 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789463669177 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MnU |
| Language of cataloging | eng |
| Transcribing agency | MnU |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA145 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA1-2040 |
| 245 00 - TITLE STATEMENT | |
| Title | Dynamic data processing |
| Remainder of title | Recursive least-squares |
| Statement of responsibility, etc | Peter Teunissen |
| 264 #2 - | |
| -- | Minneapolis, MN |
| -- | Open Textbook Library |
| 264 #1 - | |
| -- | [Place of publication not identified] |
| -- | TU Delft Open |
| -- | 2024. |
| 264 #4 - | |
| -- | ©2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 490 0# - SERIES STATEMENT | |
| Series statement | Open textbook library. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Introduction -- Least-squares: a review -- Recursive least-squares: the static case -- Recursive least-squares: the time-varying case -- State-space models for dynamic systems -- Random functions -- Recursive least-squares: the dynamic case -- Literature -- Index |
| 520 0# - SUMMARY, ETC. | |
| Summary, etc | This book is a follow-up on Adjustment theory. It extends the theory to the case of time-varying parameters with an emphasis on their recursive determination. Least-squares estimation will be the leading principle used. A least-squares solution is said to be recursive when the method of computation enables sequential, rather than batch, processing of the measurement data. The recursive equations enable the updating of parameter estimates for new observations without the need to store all past observations. Methods of recursive least-squares estimation are therefore particularly useful for applications in which the time-varying parameters need to be instantly determined. Important examples of such applications can be found in the fields of real-time kinematic positioning, navigation and guidance, or multivariate time series analysis. The goal of this book is therefore to convey the necessary knowledge to be able to process sequentially collected measurements for the purpose of estimating time-varying parameters. When determining time-varying parameters from sequentially collected measurement data, one can discriminate between three types of estimation problems: filtering, prediction and smoothing. Filtering aims at the determination of current parameter values, while smoothing and prediction aim at the determination of respectively past and future parameter values. The emphasis in this book will be on recursive least-squares filtering. The theory is worked out for the important case of linear(ized) models. The measurement-update and time-update equations of recursive least-squares are discussed in detail. Models with sequentially collected data, but time-invariant parameters are treated first. In this case only the measurement-update equations apply. State-space models for dynamic systems are discussed so as to include time-varying parameters. This includes their linearization and the construction of the state transition matrix. Elements from the theory of random functions are used to describe the propagation laws for linear dynamic systems. The theory is illustrated by means of many worked out examples. They are drawn from applications such as kinematic positioning, satellite orbit determination and inertial navigation. |
| 542 1# - | |
| -- | Attribution |
| 546 ## - LANGUAGE NOTE | |
| Language note | In English. |
| 588 0# - | |
| -- | Description based on print resource |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Engineering and Technology |
| Form subdivision | Textbooks |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Civil Engineering |
| Form subdivision | Textbooks |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Teunissen, Peter J.G. |
| Relator term | author |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Open Textbook Library |
| Relator term | distributor |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://open.umn.edu/opentextbooks/textbooks/1727">https://open.umn.edu/opentextbooks/textbooks/1727</a> |
| Public note | Access online version |
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