| 書(shū)目名稱 | Linear Estimation and Detection in Krylov Subspaces | | 編輯 | Guido K.E. Dietl | | 視頻video | http://file.papertrans.cn/587/586314/586314.mp4 | | 概述 | Comprehensive overview of linear estimation algorithms | | 叢書(shū)名稱 | Foundations in Signal Processing, Communications and Networking | | 圖書(shū)封面 |  | | 描述 | One major area in the theory of statistical signal processing is reduced-rank - timation where optimal linear estimators are approximated in low-dimensional subspaces, e.g., in order to reduce the noise in overmodeled problems, - hance the performance in case of estimated statistics, and/or save compu- tional complexity in the design of the estimator which requires the solution of linear equation systems. This book provides a comprehensive overview over reduced-rank ?lters where the main emphasis is put on matrix-valued ?lters whose design requires the solution of linear systems with multiple right-hand sides. In particular, the multistage matrix Wiener ?lter, i.e., a reduced-rank Wiener ?lter based on the multistage decomposition, is derived in its most general form. In numerical mathematics, iterative block Krylov methods are very po- lar techniques for solving systems of linear equations with multiple right-hand sides, especially if the systems are large and sparse. Besides presenting a - tailed overview of the most important block Krylov methods in Chapter 3, which may also serve as an introduction to the topic, their connection to the multistage matrix Wiener ?lter is revealed | | 出版日期 | Book 2007 | | 關(guān)鍵詞 | Monte-Carlo Simulation; Signal; communication; complexity; information; model; simulation | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-540-68479-4 | | isbn_softcover | 978-3-642-08803-2 | | isbn_ebook | 978-3-540-68479-4Series ISSN 1863-8538 Series E-ISSN 1863-8546 | | issn_series | 1863-8538 | | copyright | Springer-Verlag Berlin Heidelberg 2007 |
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