02573oam 2200697 450 991070840090332120181004143403.0(CKB)25435299800041(OCoLC)1002634486(EXLCZ)992543529980004120170901d2017 ua 0engurmn|||||||||txtrdacontentcrdamediacrrdacarrierRotational deployments vs. forward stationing how can the Army achieve assurance and deterrence efficiently and effectively? /John R. DeniCarlisle, PA :Strategic Studies Institute and U.S. Army War College Press,2017.1 online resource (xxii, 53 pages) color illustrations, color maps"August 2017."Paper version available for sale by the Superintendent of Documents, U.S. Government Publishing Office.Includes bibliographical references (pages 44-53).Rotational deployments versus forward stationing :how can the Army achieve assurance and deterrence efficiently and effectively?How can the Army achieve assurance and deterrence efficiently and effectively?Deployment (Strategy)Military planningUnited StatesMilitary bases, AmericanForeign countriesStrategic aspectsArmed ForcesOperational readinessfastArmed ForcesOrganizationfastArmed ForcesPersonnel managementfastDeployment (Strategy)fastMilitary planningfastUnited StatesfastDeployment (Strategy)Military planningMilitary bases, AmericanStrategic aspects.Armed ForcesOperational readiness.Armed ForcesOrganization.Armed ForcesPersonnel management.Deployment (Strategy)Military planning.Deni John R.1401170Army War College (U.S.).Strategic Studies Institute,Army War College (U.S.).Press,AWCAWCNLGGCOCLCFGPOOCLCQOCLCOOCLCAOCLLWAUOKINTGPOBOOK9910708400903321Rotational deployments vs. forward stationing3487605UNINA03899nam 2200481 450 991081641210332120230822173031.01-119-45777-71-119-45769-6(CKB)4100000007815535(CaSebORM)9781119457763(MiAaPQ)EBC5732749(OCoLC)1057238048(EXLCZ)99410000000781553520190329d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierModel-based processing an applied subspace identification approach /James V. Candy1st editionHoboken, NJ :John Wiley & Sons, Inc.,2019.1 online resource (540 pages)THEi Wiley ebooks.1-119-45776-9 Includes bibliographical references and index.A 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.Signal processingDigital techniquesMathematicsAutomatic controlMathematical modelsInvariant subspacesSignal processingDigital techniquesMathematics.Automatic controlMathematical models.Invariant subspaces.621.382/23Candy James V.8471MiAaPQMiAaPQMiAaPQBOOK9910816412103321Model-based processing4117156UNINA