Книга "Advanced Kalman Filtering, Least-Squares and Modeling. A Practical Handbook" - это практическое руководство, которое рассчитано в первую очередь на инженеров, занимающихся проектированием систем. Она поможет читателю разработать оценщик, который будет соответствовать всем требованиям приложения и будет устойчив к предположениям моделирования. Авторы обсуждают методы разработки модели с достаточной детализацией, чтобы читатель мог спроектировать оценщик, который будет соответствовать всем требованиям. Также обсуждается использование методов анализа исходных данных для определения структуры модели.
Книга также представляет малоизвестные расширения методов наименьших квадратов и фильтра Калмана, которые позволяют определить структуру и параметры модели или сделать оценщик более устойчивым к изменениям в реальном мире. Она также обсуждает вопросы реализации, которые делают оценщик более точным и эффективным, а также более гибким, чтобы можно было легко сравнивать альтернативные модели.
Книга предоставляет руководство по оценке производительности оценщика и определению/исправлению проблем. Также в книге представлена библиотека подпрограмм, которая упрощает реализацию, и гибкие универсальные драйверы, которые позволяют как легко анализировать альтернативные модели, так и получить доступ к расширениям базового фильтра. Дополнительные материалы и актуальные исправления можно загрузить на сайте http://booksupport.wiley.com.
This book is written primarily for engineers designing practical systems, and its main goal is not just to explain model development, but to enable the reader to create estimators that perform optimally to meet all practical requirements and still maintain a robust foundation based on assumptions. Inevitably, most designs depend on trying to identify the optimal model structure from the outset; this volume includes guidance for exploratory data analyses to help specify model structure. It also provides readers with an intuitive overview of several minor extensions to the basic least squares and Kalman filter algorithms that help designers exercise more control over model architecture and explain more easily to unreliable growth in reality's work performance and how to adjust accordingly. The third purpose is to explore limiting desktop designs that enhance accuracy, efficiency, or flexibility, which will help lesser systems analysts and designers compare their options. Engineers are often required to accomplish fast filtering, and these techniques include streaming and incremental processing methods to increase efficiency of analysis. The fourth aim is to help providers as well as evaluation and identifying instances where transformative techniques might need to be utilized, additionally letting them enumerate and solve implementation troubles. Finally, this book includes a much needed library containing capacity routines (subroutines) to make configuration simpler. There is also designed general-purpose higher-level drivers accessible, enabling both simple scrutiny of various architectures and quick exploration of unconventional methods to enhance the base model. All supplementary supplies and updates are accessible at the company's webpage.
This book is intended for practicing engineers engaged in system design. Its main aim has been to cover model and filter design issues fully, enabling the reader to select a filter configuration that meets their target characteristics and withstands assumptions about model/signal dynamics necessitated for optimal filters. Given that real world conditions vary considerably, it may be quite beneficial to employ a data driven approach for model selection. Estimation techniques presented in this handbook make extensions to such efforts possible, offering you new planning directions and measures. Further, techniques include ways to enhance robustness in the context of variations in system behavior, thus “pushing to the limits” of what the algorithm can provide. One last idea in play is working out the functional and good implementation details, making filters more stable, faster, adaptable, and readable to the configurational discipline. As for filling gaps in the hands-on process, we’ve also endeavored to translate valuable, if somewhat esoteric knowledge to literally get you started with applications. Our focus is that these practical topics would drive you to successful completion of the filter projects for various applications your team engages in. To support this, there is a furnished utility support site, where providers, methods, errata, and resources are available at hand, continuously updated. Bypass building this manual from scratch. Therefore, your job is getting a hands-on filter project under your belt and spanning new approaches to system simulation without forcing you into the theoretical nitty gritty.
Электронная Книга «Advanced Kalman Filtering, Least-Squares and Modeling. A Practical Handbook» написана автором Bruce Gibbs P. в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9780470890035
Описание книги от Bruce Gibbs P.
This book is intended primarily as a handbook for engineers who must design practical systems. Its primary goal is to discuss model development in sufficient detail so that the reader may design an estimator that meets all application requirements and is robust to modeling assumptions. Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed. Methods for deciding on the “best” model are also presented. A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior. A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared. The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems. The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering. Supplemental materials and up-to-date errata are downloadable at http://booksupport.wiley.com.
