书目名称 | Fixed Interval Smoothing for State Space Models | 编辑 | Howard L. Weinert | 视频video | http://file.papertrans.cn/345/344029/344029.mp4 | 丛书名称 | The Springer International Series in Engineering and Computer Science | 图书封面 |  | 描述 | Fixed-interval smoothing is a method of extracting usefulinformation from inaccurate data. It has been applied to problems inengineering, the physical sciences, and the social sciences, in areassuch as control, communications, signal processing, acoustics,geophysics, oceanography, statistics, econometrics, and structuralanalysis. .This monograph addresses problems for which a linear stochastic statespace model is available, in which case the objective is to computethe linear least-squares estimate of the state vector in a fixedinterval, using observations previously collected in that interval.The author uses a geometric approach based on the method ofcomplementary models. Using the simplest possible notation, hepresents straightforward derivations of the four types offixed-interval smoothing algorithms, and compares the algorithms interms of efficiency and applicability. Results show that the bestalgorithm has received the least attention in the literature. ..Fixed Interval Smoothing for State Space Models:. .. includes new material on interpolation, fast square rootimplementations, and boundary value models; .. is the first bookdevoted to smoothing; .. contains an annotated biblio | 出版日期 | Book 2001 | 关键词 | Signal; acoustics; communication; filters; information; metrics; model; signal processing; statistics | 版次 | 1 | doi | https://doi.org/10.1007/978-1-4615-1691-0 | isbn_softcover | 978-1-4613-5680-6 | isbn_ebook | 978-1-4615-1691-0Series ISSN 0893-3405 | issn_series | 0893-3405 | copyright | Springer Science+Business Media New York 2001 |
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