Description
Taylor and Francis Optimal Estimation Of Dynamic Systems 2Nd Edition 2012 Edition by John L. Crassidis, John L. Junkins
Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB (R) codes for the examples are available on the book's website.New to the Second EditionWith more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises. An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking. Least Squares ApproximationA Curve Fitting ExampleLinear Batch EstimationLinear Sequential EstimationNonlinear Least Squares EstimationBasis FunctionsAdvanced TopicsProbability Concepts in Least SquaresMinimum Variance EstimationUnbiased EstimatesMaximum Likelihood EstimationCramer-Rao InequalityConstrained Least Squares Covariance Maximum Likelihood EstimationProperties of Maximum Likelihood EstimationBayesian EstimationAdvanced TopicsSequential State EstimationA Simple First-Order Filter ExampleFull-Order EstimatorsThe Discrete-Time Kalman FilterThe Continuous-Time Kalman FilterThe Continuous-Discrete Kalman FilterExtended Kalman FilterUnscented FilteringConstrained FilteringAdvanced Topics in Sequential State EstimationFactorization MethodsColored-Noise Kalman FilteringConsistency of the Kalman FilterConsider Kalman FilteringDecentralized FilteringAdaptive FilteringEnsemble Kalman FilteringNonlinear Stochastic Filtering TheoryGaussian Sum FilteringParticle FilteringError AnalysisRobust FilteringBatch State EstimationFixed-Interval SmoothingFixed-Point SmoothingFixed-Lag SmoothingAdvanced TopicsParameter Estimation: ApplicationsAttitude DeterminationGlobal Positioning System NavigationSimultaneous Localization and MappingOrbit DeterminationAircraft Parameter IdentificationEigensystem Realization AlgorithmEstimation of Dynamic Systems: ApplicationsAttitude EstimationInertial Navigation with GPSOrbit EstimationTarget Tracking of AircraftSmoothing with the Eigensystem Realization AlgorithmOptimal Control and Estimation TheoryCalculus of VariationsOptimization with Differential Equation ConstraintsPontryagin's Optimal Control Necessary ConditionsDiscrete-Time ControlLinear Regulator ProblemsLinear Quadratic-Gaussian ControllersLoop Transfer RecoverySpacecraft Control DesignAppendix A: Review of Dynamical SystemsAppendix B: Matrix PropertiesAppendix C: Basic Probability ConceptsAppendix D: Parameter Optimization MethodsAppendix E: Computer SoftwareIndexA Summary appears at the end of each chapter.