LEADER 03899nam 2200481 450 001 9910816412103321 005 20230822173031.0 010 $a1-119-45777-7 010 $a1-119-45769-6 035 $a(CKB)4100000007815535 035 $a(CaSebORM)9781119457763 035 $a(MiAaPQ)EBC5732749 035 $a(OCoLC)1057238048 035 $a(EXLCZ)994100000007815535 100 $a20190329d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModel-based processing $ean applied subspace identification approach /$fJames V. Candy 205 $a1st edition 210 1$aHoboken, NJ :$cJohn Wiley & Sons, Inc.,$d2019. 215 $a1 online resource (540 pages) 225 0 $aTHEi Wiley ebooks. 311 $a1-119-45776-9 320 $aIncludes bibliographical references and index. 330 $aA bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems Model-Based Processing : An Applied Subspace Identification Approach provides 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. The 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: Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters Practical processor designs including comprehensive methods of performance analysis Provides a link between model development and practical applications in model-based signal processing Offers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications Enables readers to bridge the gap from statistical signal processing to subspace identification Includes appendices, problem sets, case studies, examples, and notes for MATLAB Model-Based Processing: An Applied Subspace Identification Approach is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia. 606 $aSignal processing$xDigital techniques$xMathematics 606 $aAutomatic control$xMathematical models 606 $aInvariant subspaces 615 0$aSignal processing$xDigital techniques$xMathematics. 615 0$aAutomatic control$xMathematical models. 615 0$aInvariant subspaces. 676 $a621.382/23 700 $aCandy$b James V.$08471 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910816412103321 996 $aModel-based processing$94117156 997 $aUNINA