05390nam 22006614a 450 991087750580332120200520144314.01-280-50795-097866105079550-470-04534-51-61583-476-10-470-04533-7(CKB)1000000000355271(EBL)265855(SSID)ssj0000215745(PQKBManifestationID)11208070(PQKBTitleCode)TC0000215745(PQKBWorkID)10204533(PQKB)11726097(MiAaPQ)EBC265855(PPN)170227774(OCoLC)85821130(FR-PaCSA)41001009(EXLCZ)99100000000035527120060209d2006 uy 0engur|n|---|||||txtccrOptimal state estimation Kalman, H [infinity] and nonlinear approaches /Dan SimonHoboken, N.J. Wiley-Intersciencec20061 online resource (554 p.)On t.p. "[infinity]" appears as the infinity symbol.0-471-70858-5 Includes bibliographical references (p. 501-520) and index.Optimal State Estimation; CONTENTS; Acknowledgments; Acronyms; List of algorithms; Introduction; PART I INTRODUCTORY MATERIAL; 1 Linear systems theory; 1.1 Matrix algebra and matrix calculus; 1.1.1 Matrix algebra; 1.1.2 The matrix inversion lemma; 1.1.3 Matrix calculus; 1.1.4 The history of matrices; 1.2 Linear systems; 1.3 Nonlinear systems; 1.4 Discretization; 1.5 Simulation; 1.5.1 Rectangular integration; 1.5.2 Trapezoidal integration; 1.5.3 Runge-Kutta integration; 1.6 Stability; 1.6.1 Continuous-time systems; 1.6.2 Discrete-time systems; 1.7 Controllability and observability1.7.1 Controllability1.7.2 Observability; 1.7.3 Stabilizability and detectability; 1.8 Summary; Problems; 2 Probability theory; 2.1 Probability; 2.2 Random variables; 2.3 Transformations of random variables; 2.4 Multiple random variables; 2.4.1 Statistical independence; 2.4.2 Multivariate statistics; 2.5 Stochastic Processes; 2.6 White noise and colored noise; 2.7 Simulating correlated noise; 2.8 Summary; Problems; 3 Least squares estimation; 3.1 Estimation of a constant; 3.2 Weighted least squares estimation; 3.3 Recursive least squares estimation; 3.3.1 Alternate estimator forms3.3.2 Curve fitting3.4 Wiener filtering; 3.4.1 Parametric filter optimization; 3.4.2 General filter optimization; 3.4.3 Noncausal filter optimization; 3.4.4 Causal filter optimization; 3.4.5 Comparison; 3.5 Summary; Problems; 4 Propagation of states and covariances; 4.1 Discrete-time systems; 4.2 Sampled-data systems; 4.3 Continuous-time systems; 4.4 Summary; Problems; PART II THE KALMAN FILTER; 5 The discrete-time Kalman filter; 5.1 Derivation of the discrete-time Kalman filter; 5.2 Kalman filter properties; 5.3 One-step Kalman filter equations; 5.4 Alternate propagation of covariance5.4.1 Multiple state systems5.4.2 Scalar systems; 5.5 Divergence issues; 5.6 Summary; Problems; 6 Alternate Kalman filter formulations; 6.1 Sequential Kalman filtering; 6.2 Information filtering; 6.3 Square root filtering; 6.3.1 Condition number; 6.3.2 The square root time-update equation; 6.3.3 Potter's square root measurement-update equation; 6.3.4 Square root measurement update via triangularization; 6.3.5 Algorithms for orthogonal transformations; 6.4 U-D filtering; 6.4.1 U-D filtering: The measurement-update equation; 6.4.2 U-D filtering: The time-update equation; 6.5 Summary; Problems7 Kalman filter generalizations7.1 Correlated process and measurement noise; 7.2 Colored process and measurement noise; 7.2.1 Colored process noise; 7.2.2 Colored measurement noise: State augmentation; 7.2.3 Colored measurement noise: Measurement differencing; 7.3 Steady-state filtering; 7.3.1 α-β filtering; 7.3.2 α-β-γ filtering; 7.3.3 A Hamiltonian approach to steady-state filtering; 7.4 Kalman filtering with fading memory; 7.5 Constrained Kalman filtering; 7.5.1 Model reduction; 7.5.2 Perfect measurements; 7.5.3 Projection approaches; 7.5.4 A pdf truncation approach; 7.6 Summary; Problems8 The continuous-time Kalman filterA bottom-up approach that enables readers to master and apply the latest techniques in state estimationThis 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.While there are other textbooks that treat state estimation, this one offers special features and a uniquKalman filteringNonlinear systemsMathematical optimizationKalman filtering.Nonlinear systems.Mathematical optimization.629.8/312Simon Dan1960-856795MiAaPQMiAaPQMiAaPQBOOK9910877505803321Optimal state estimation4191156UNINA