Smoothing, Filtering and Prediction
This book describes the classical smoothing, filtering and prediction techniques together with some more recently developed embellishments for improving performance within applications. It aims to present the subject in an accessible way, so that it can serve as a practical guide for undergraduates...
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| Format: | Online |
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| Language: | English |
| Published: |
IntechOpen
2023
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| Online Access: | ONIX_20231201_9789533077529_234 |
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| _version_ | 1869517699790405632 |
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| collection | Directory of Open Access Books |
| description | This book describes the classical smoothing, filtering and prediction techniques together with some more recently developed embellishments for improving performance within applications. It aims to present the subject in an accessible way, so that it can serve as a practical guide for undergraduates and newcomers to the field. The material is organised as a ten-lecture course. The foundations are laid in Chapters 1 – 2, which explain minimum-mean-square-error solution construction and asymptotic behaviour. Chapters 3 and 4 introduce continuous-time and discrete-time minimum-variance filtering. Generalisations for missing data, deterministic inputs, correlated noises, direct feedthrough terms, output estimation and equalisation are described. Chapter 5 simplifies the minimum-variance filtering results for steady-state problems. Observability, Riccati equation solution convergence, asymptotic stability and Wiener filter equivalence are discussed. Chapters 6 and 7 cover the subject of continuous-time and discrete-time smoothing. The main fixed-lag, fixed-point and fixed-interval smoother results are derived. It is shown that the minimum-variance fixed-interval smoother attains the best performance. Chapter 8 attends to parameter estimation. As the above-mentioned approaches all rely on knowledge of the underlying model parameters, maximum-likelihood techniques within expectation-maximisation algorithms for joint state and parameter estimation are described. Chapter 9 is concerned with robust techniques that accommodate uncertainties within problem specifications. An extra term within Riccati equations enables designers to trade-off average error and peak error performance. Chapter 10 rounds off the course by applying the afore-mentioned linear techniques to nonlinear estimation problems. It is demonstrated that step-wise linearisations can be used within predictors, filters and smoothers, albeit by forsaking optimal performance guarantees. |
| format | Online |
| id | doab-20.500.12854ir-129127 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1291272024-04-04T14:41:21Z Smoothing, Filtering and Prediction Einicke, Garry A. Applied mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling This book describes the classical smoothing, filtering and prediction techniques together with some more recently developed embellishments for improving performance within applications. It aims to present the subject in an accessible way, so that it can serve as a practical guide for undergraduates and newcomers to the field. The material is organised as a ten-lecture course. The foundations are laid in Chapters 1 – 2, which explain minimum-mean-square-error solution construction and asymptotic behaviour. Chapters 3 and 4 introduce continuous-time and discrete-time minimum-variance filtering. Generalisations for missing data, deterministic inputs, correlated noises, direct feedthrough terms, output estimation and equalisation are described. Chapter 5 simplifies the minimum-variance filtering results for steady-state problems. Observability, Riccati equation solution convergence, asymptotic stability and Wiener filter equivalence are discussed. Chapters 6 and 7 cover the subject of continuous-time and discrete-time smoothing. The main fixed-lag, fixed-point and fixed-interval smoother results are derived. It is shown that the minimum-variance fixed-interval smoother attains the best performance. Chapter 8 attends to parameter estimation. As the above-mentioned approaches all rely on knowledge of the underlying model parameters, maximum-likelihood techniques within expectation-maximisation algorithms for joint state and parameter estimation are described. Chapter 9 is concerned with robust techniques that accommodate uncertainties within problem specifications. An extra term within Riccati equations enables designers to trade-off average error and peak error performance. Chapter 10 rounds off the course by applying the afore-mentioned linear techniques to nonlinear estimation problems. It is demonstrated that step-wise linearisations can be used within predictors, filters and smoothers, albeit by forsaking optimal performance guarantees. 2023-12-01T14:52:35Z 2023-12-01T14:52:35Z 2012 book ONIX_20231201_9789533077529_234 9789533077529 9789535143468 https://directory.doabooks.org/handle/20.500.12854/129127 eng image/jpeg n/a https://www.intechopen.com/books/2985 https://mts.intechopen.com/storage/books/2985/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/2706 10.5772/2706 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9789533077529 9789535143468 IntechOpen 288 open access |
| spellingShingle | Applied mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling Smoothing, Filtering and Prediction |
| title | Smoothing, Filtering and Prediction |
| title_full | Smoothing, Filtering and Prediction |
| title_fullStr | Smoothing, Filtering and Prediction |
| title_full_unstemmed | Smoothing, Filtering and Prediction |
| title_short | Smoothing, Filtering and Prediction |
| title_sort | smoothing filtering and prediction |
| topic | Applied mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling |
| topic_facet | Applied mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling |
| url | ONIX_20231201_9789533077529_234 |