{"product_id":"optimal-state-estimation-kalman-h-infinity-and-nonlinear-approaches-hardback-9780471708582","title":"Optimal State Estimation; Kalman, H Infinity, and Nonlinear Approaches (Hardback) 9780471708582","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOptimal State Estimation\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eKalman, H Infinity, and Nonlinear Approaches\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eDan Simon (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471708582, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 21 July 2006\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e552 pages\u003cbr\u003e26.2 x 18.5 x 3.6 cm, 1.17 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"This book is obviously written with care and reads very easily. A very valuable resource for students, teachers, and practitioners…highly recommended.\" (\u003ci\u003eCHOICE\u003c\/i\u003e, February 2007)  \u003cp\u003e\"The dozens of helpful step-by-step examples, visual illustrations, and lists of exercises proposed at the end of each chapter significantly facilitate a reader's understanding of the book's content.\" (\u003ci\u003eComputing Reviews.com\u003c\/i\u003e, December 4, 2006)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eA bottom-up approach that enables readers to master and apply the latest techniques in state estimation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering.\u003c\/p\u003e \u003cp\u003eWhile there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eStraightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation\u003c\/li\u003e \u003cli\u003eSimple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice\u003c\/li\u003e \u003cli\u003eMATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eArmed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman\/H? filtering.\u003c\/p\u003e \u003cp\u003eProblems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, \u003ci\u003eOptimal State Estimation\u003c\/i\u003e is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eAcknowledgments. \u003cp\u003eAcronyms.\u003c\/p\u003e \u003cp\u003eList of algorithms.\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I INTRODUCTORY MATERIAL.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Linear systems theory.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Matrix algebra and matrix calculus.\u003c\/p\u003e \u003cp\u003e1.1.1 Matrix algebra.\u003c\/p\u003e \u003cp\u003e1.1.2 The matrix inversion lemma.\u003c\/p\u003e \u003cp\u003e1.1.3 Matrix calculus.\u003c\/p\u003e \u003cp\u003e1.1.4 The history of matrices.\u003c\/p\u003e \u003cp\u003e1.2 Linear systems.\u003c\/p\u003e \u003cp\u003e1.3 Nonlinear systems.\u003c\/p\u003e \u003cp\u003e1.4 Discretization.\u003c\/p\u003e \u003cp\u003e1.5 Simulation.\u003c\/p\u003e \u003cp\u003e1.5.1 Rectangular integration.\u003c\/p\u003e \u003cp\u003e1.5.2 Trapezoidal integration.\u003c\/p\u003e \u003cp\u003e1.5.3 RungeKutta integration.\u003c\/p\u003e \u003cp\u003e1.6 Stability.\u003c\/p\u003e \u003cp\u003e1.6.1 Continuous-time systems.\u003c\/p\u003e \u003cp\u003e1.6.2 Discretetime systems.\u003c\/p\u003e \u003cp\u003e1.7 Controllability and observability.\u003c\/p\u003e \u003cp\u003e1.7.1 Controllability.\u003c\/p\u003e \u003cp\u003e1.7.2 Observability.\u003c\/p\u003e \u003cp\u003e1.7.3 Stabilizability and detectability.\u003c\/p\u003e \u003cp\u003e1.8 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Probability theory.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Probability.\u003c\/p\u003e \u003cp\u003e2.2 Random variables.\u003c\/p\u003e \u003cp\u003e2.3 Transformations of random variables.\u003c\/p\u003e \u003cp\u003e2.4 Multiple random variables.\u003c\/p\u003e \u003cp\u003e2.4.1 Statistical independence.\u003c\/p\u003e \u003cp\u003e2.4.2 Multivariate statistics.\u003c\/p\u003e \u003cp\u003e2.5 Stochastic Processes.\u003c\/p\u003e \u003cp\u003e2.6 White noise and colored noise.\u003c\/p\u003e \u003cp\u003e2.7 Simulating correlated noise.\u003c\/p\u003e \u003cp\u003e2.8 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Least squares estimation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Estimation of a constant.\u003c\/p\u003e \u003cp\u003e3.2 Weighted least squares estimation.\u003c\/p\u003e \u003cp\u003e3.3 Recursive least squares estimation.\u003c\/p\u003e \u003cp\u003e3.3.1 Alternate estimator forms.\u003c\/p\u003e \u003cp\u003e3.3.2 Curve fitting.\u003c\/p\u003e \u003cp\u003e3.4 Wiener filtering.\u003c\/p\u003e \u003cp\u003e3.4.1 Parametric filter optimization.\u003c\/p\u003e \u003cp\u003e3.4.2 General filter optimization.\u003c\/p\u003e \u003cp\u003e3.4.3 Noncausal filter optimization.\u003c\/p\u003e \u003cp\u003e3.4.4 Causal filter optimization.\u003c\/p\u003e \u003cp\u003e3.4.5 Comparison.\u003c\/p\u003e \u003cp\u003e3.5 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Propagation of states and covariances.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Discretetime systems.\u003c\/p\u003e \u003cp\u003e4.2 Sampled-data systems.\u003c\/p\u003e \u003cp\u003e4.3 Continuous-time systems.\u003c\/p\u003e \u003cp\u003e4.4 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003ePART II THE KALMAN FILTER.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The discrete-time Kalman filter.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Derivation of the discrete-time Kalman filter.\u003c\/p\u003e \u003cp\u003e5.2 Kalman filter properties.\u003c\/p\u003e \u003cp\u003e5.3 One-step Kalman filter equations.\u003c\/p\u003e \u003cp\u003e5.4 Alternate propagation of covariance.\u003c\/p\u003e \u003cp\u003e5.4.1 Multiple state systems.\u003c\/p\u003e \u003cp\u003e5.4.2 Scalar systems.\u003c\/p\u003e \u003cp\u003e5.5 Divergence issues.\u003c\/p\u003e \u003cp\u003e5.6 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Alternate Kalman filter formulations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Sequential Kalman filtering.\u003c\/p\u003e \u003cp\u003e6.2 Information filtering.\u003c\/p\u003e \u003cp\u003e6.3 Square root filtering.\u003c\/p\u003e \u003cp\u003e6.3.1 Condition number.\u003c\/p\u003e \u003cp\u003e6.3.2 The square root time-update equation.\u003c\/p\u003e \u003cp\u003e6.3.3 Potter's square root measurement-update equation.\u003c\/p\u003e \u003cp\u003e6.3.4 Square root measurement update via triangularization.\u003c\/p\u003e \u003cp\u003e6.3.5 Algorithms for orthogonal transformations.\u003c\/p\u003e \u003cp\u003e6.4 U-D filtering.\u003c\/p\u003e \u003cp\u003e6.4.1 U-D filtering: The measurement-update equation.\u003c\/p\u003e \u003cp\u003e6.4.2 U-D filtering: The time-update equation.\u003c\/p\u003e \u003cp\u003e6.5 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Kalman filter generalizations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Correlated process and measurement noise.\u003c\/p\u003e \u003cp\u003e7.2 Colored process and measurement noise.\u003c\/p\u003e \u003cp\u003e7.2.1 Colored process noise.\u003c\/p\u003e \u003cp\u003e7.2.2 Colored measurement noise: State augmentation.\u003c\/p\u003e \u003cp\u003e7.2.3 Colored measurement noise: Measurement differencing.\u003c\/p\u003e \u003cp\u003e7.3 Steady-state filtering.\u003c\/p\u003e \u003cp\u003e7.3.1 \u003ci\u003ea-P\u003c\/i\u003e filtering.\u003c\/p\u003e \u003cp\u003e7.3.2 \u003ci\u003ea-P-y\u003c\/i\u003e filtering.\u003c\/p\u003e \u003cp\u003e7.3.3 A Hamiltonian approach to steady-state filtering.\u003c\/p\u003e \u003cp\u003e7.4 Kalman filtering with fading memory.\u003c\/p\u003e \u003cp\u003e7.5 Constrained Kalman filtering.\u003c\/p\u003e \u003cp\u003e7.5.1 Model reduction.\u003c\/p\u003e \u003cp\u003e7.5.2 Perfect measurements.\u003c\/p\u003e \u003cp\u003e7.5.3 Projection approaches.\u003c\/p\u003e \u003cp\u003e7.5.4 A pdf truncation approach.\u003c\/p\u003e \u003cp\u003e7.6 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The continuous-time Kalman filter.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Discrete-time and continuous-time white noise.\u003c\/p\u003e \u003cp\u003e8.1.1 Process noise.\u003c\/p\u003e \u003cp\u003e8.1.2 Measurement noise.\u003c\/p\u003e \u003cp\u003e8.1.3 Discretized simulation of noisy continuous-time systems.\u003c\/p\u003e \u003cp\u003e8.2 Derivation of the continuous-time Kalman filter.\u003c\/p\u003e \u003cp\u003e8.3 Alternate solutions to the Riccati equation.\u003c\/p\u003e \u003cp\u003e8.3.1 The transition matrix approach.\u003c\/p\u003e \u003cp\u003e8.3.2 The Chandrasekhar algorithm.\u003c\/p\u003e \u003cp\u003e8.3.3 The square root filter.\u003c\/p\u003e \u003cp\u003e8.4 Generalizations of the continuous-time filter.\u003c\/p\u003e \u003cp\u003e8.4.1 Correlated process and measurement noise.\u003c\/p\u003e \u003cp\u003e8.4.2 Colored measurement noise\u003c\/p\u003e \u003cp\u003e8.5 The steady-state continuous-time Kalman filter\u003c\/p\u003e \u003cp\u003e8.5.1 The algebraic Riccati equation.\u003c\/p\u003e \u003cp\u003e8.5.2 The Wiener filter is a Kalman filter.\u003c\/p\u003e \u003cp\u003e8.5.3 Duality.\u003c\/p\u003e \u003cp\u003e8.6 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Optimal smoothing.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 An alternate form for the Kalman filter.\u003c\/p\u003e \u003cp\u003e9.2 Fixed-point smoothing.\u003c\/p\u003e \u003cp\u003e9.2.1 Estimation improvement due to smoothing.\u003c\/p\u003e \u003cp\u003e9.2.2 Smoothing constant states.\u003c\/p\u003e \u003cp\u003e9.3 Fixed-lag smoothing.\u003c\/p\u003e \u003cp\u003e9.4 Fixed-interval smoothing.\u003c\/p\u003e \u003cp\u003e9.4.1 Forward-backward smoothing.\u003c\/p\u003e \u003cp\u003e9.4.2 RTS smoothing.\u003c\/p\u003e \u003cp\u003e9.5 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Additional topics in Kalman filtering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Verifying Kalman filter performance.\u003c\/p\u003e \u003cp\u003e10.2 Multiple-model estimation.\u003c\/p\u003e \u003cp\u003e10.3 Reduced-order Kalman filtering.\u003c\/p\u003e \u003cp\u003e10.3.1 Anderson's approach to reduced-order filtering.\u003c\/p\u003e \u003cp\u003e10.3.2 The reduced-order Schmidt-Kalman filter.\u003c\/p\u003e \u003cp\u003e10.4 Robust Kalman filtering.\u003c\/p\u003e \u003cp\u003e10.5 Delayed measurements and synchronization errors.\u003c\/p\u003e \u003cp\u003e10.5.1 A statistical derivation of the Kalman filter.\u003c\/p\u003e \u003cp\u003e10.5.2 Kalman filtering with delayed measurements.\u003c\/p\u003e \u003cp\u003e10.6 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003ePART III THE H, FILTER.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The H, filter.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.1.1 An alternate form for the Kalman filter.\u003c\/p\u003e \u003cp\u003e11.1.2 Kalman filter limitations.\u003c\/p\u003e \u003cp\u003e11.2 Constrained optimization.\u003c\/p\u003e \u003cp\u003e11.2.1 Static constrained optimization.\u003c\/p\u003e \u003cp\u003e11.2.2 Inequality constraints.\u003c\/p\u003e \u003cp\u003e11.2.3 Dynamic constrained optimization.\u003c\/p\u003e \u003cp\u003e11.3 A game theory approach to H, filtering.\u003c\/p\u003e \u003cp\u003e11.3.1 Stationarity with respect to \u003ci\u003exo\u003c\/i\u003e and \u003ci\u003ewk.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.3.2 Stationarity with respect to \u003ci\u003e2 and\u003c\/i\u003e y.\u003c\/p\u003e \u003cp\u003e11.3.3 A comparison of the Kalman and H, filters.\u003c\/p\u003e \u003cp\u003e11.3.4 Steady-state H, filtering.\u003c\/p\u003e \u003cp\u003e11.3.5 The transfer function bound of the H, filter.\u003c\/p\u003e \u003cp\u003e11.4 The continuous-time H, filter.\u003c\/p\u003e \u003cp\u003e11.5 Transfer function approaches.\u003c\/p\u003e \u003cp\u003e11.6 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Additional topics in H, filtering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Mixed KalmanIH, filtering.\u003c\/p\u003e \u003cp\u003e12.2 Robust Kalman\/H, filtering.\u003c\/p\u003e \u003cp\u003e12.3 Constrained H, filtering.\u003c\/p\u003e \u003cp\u003e12.4 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003ePART IV NONLINEAR FILTERS.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Nonlinear Kalman filtering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 The linearized Kalman filter.\u003c\/p\u003e \u003cp\u003e13.2 The extended Kalman filter.\u003c\/p\u003e \u003cp\u003e13.2.1 The continuous-time extended Kalman filter.\u003c\/p\u003e \u003cp\u003e13.2.2 The hybrid extended Kalman filter.\u003c\/p\u003e \u003cp\u003e13.2.3 The discrete-time extended Kalman filter.\u003c\/p\u003e \u003cp\u003e13.3 Higher-order approaches.\u003c\/p\u003e \u003cp\u003e13.3.1 The iterated extended Kalman filter.\u003c\/p\u003e \u003cp\u003e13.3.2 The second-order extended Kalman filter.\u003c\/p\u003e \u003cp\u003e13.3.3 Other approaches.\u003c\/p\u003e \u003cp\u003e13.4 Parameter estimation.\u003c\/p\u003e \u003cp\u003e13.5 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 The unscented Kalman filter.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Means and covariances of nonlinear transformations.\u003c\/p\u003e \u003cp\u003e14.1.1 The mean of a nonlinear transformation.\u003c\/p\u003e \u003cp\u003e14.1.2 The covariance of a nonlinear transformation.\u003c\/p\u003e \u003cp\u003e14.2 Unscented transformations.\u003c\/p\u003e \u003cp\u003e14.2.1 Mean approximation.\u003c\/p\u003e \u003cp\u003e14.2.2 Covariance approximation.\u003c\/p\u003e \u003cp\u003e14.3 Unscented Kalman filtering.\u003c\/p\u003e \u003cp\u003e14.4 Other unscented transformations.\u003c\/p\u003e \u003cp\u003e14.4.1 General unscented transformations.\u003c\/p\u003e \u003cp\u003e14.4.2 The simplex unscented transformation.\u003c\/p\u003e \u003cp\u003e14.4.3 The spherical unscented transformation.\u003c\/p\u003e \u003cp\u003e14.5 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 The particle filter.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Bayesian state estimation.\u003c\/p\u003e \u003cp\u003e15.2 Particle filtering.\u003c\/p\u003e \u003cp\u003e15.3 Implementation issues.\u003c\/p\u003e \u003cp\u003e15.3.1 Sample impoverishment.\u003c\/p\u003e \u003cp\u003e15.3.2 Particle filtering combined with other filters.\u003c\/p\u003e \u003cp\u003e15.4 Summary.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eAppendix A: Historical perspectives.\u003c\/p\u003e \u003cp\u003eAppendix B: Other books on Kalman filtering.\u003c\/p\u003e \u003cp\u003eAppendix C: State estimation and the meaning of life.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand 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