{"product_id":"9781119457763","title":"Model-Based Processing An Applied Subspace Identification Approach","description":"\u003ch1\u003eModel-Based Processing\u003c\/h1\u003e\u003ch2\u003eAn Applied Subspace Identification Approach\u003c\/h2\u003e\u003ch3\u003eJames V. Candy\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eTechnology \u0026amp; Engineering \/ Signals \u0026amp; Signal Processing\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cb\u003eA bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003ci\u003eModel-Based Processing\u003c\/i\u003e:\u003ci\u003e An Applied Subspace Identification Approach \u003c\/i\u003eprovides expert insight on developing models for designing model-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. \u003c\/p\u003e \u003cp\u003eThe extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles—all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eKalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters\u003c\/li\u003e \u003cli\u003ePractical processor designs including comprehensive methods of performance analysis\u003c\/li\u003e \u003cli\u003eProvides a link between model development and practical applications in model-based signal processing\u003c\/li\u003e \u003cli\u003eOffers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications\u003c\/li\u003e \u003cli\u003eEnables readers to bridge the gap from statistical signal processing to subspace identification\u003c\/li\u003e \u003cli\u003eIncludes appendices, problem sets, case studies, examples, and notes for MATLAB\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eModel-Based Processing: An Applied Subspace Identification Approach\u003c\/i\u003e is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia. \u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e  \u003cp\u003e\u003cb\u003eJAMES V. CANDY, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Chief Scientist for Engineering, Distinguished Member of the Technical Staff, and founder of the Center for Advanced Signal \u0026amp; Image Sciences (CASIS), Lawrence Livermore National Laboratory, Livermore, California. Dr. Candy is also Adjunct Full-Professor, University of California, Santa Barbara, a Fellow of the IEEE, and a Fellow of the Acoustical Society of America. He is author of \u003ci\u003eBayesian Signal Processing: Classical, Modern, and Particle Filtering Methods\u003c\/i\u003e and \u003ci\u003eModel-Based Signal Processing\u003c\/i\u003e (John Wiley \u0026amp; Sons, Inc., 2006) and \u003ci\u003eBayesian Signal Processing: Classical, Modern and Particle Filtering Methods, Second Edition\u003c\/i\u003e (John Wiley \u0026amp; Sons, Inc., 2016). Dr. Candy was awarded the IEEE Distinguished Technical Achievement Award for his development of model-based signal processing and the Acoustical Society of America Helmholtz-Rayleigh Interdisciplinary Silver Medal for his contributions to acoustical signal processing and underwater acoustics. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e19 March 2019\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781119457763\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e544\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e35.2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44384315605132,"sku":"9781119457763","price":145.76,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781119457763_2dcdcebf-889a-41c0-b72a-a3cda00ca5ee.jpg?v=1780235122","url":"https:\/\/lateknightbooks.com\/products\/9781119457763","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}