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005 20221123104630.0
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008 031016s2001 nyua ob 001 0 eng d
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019 _a85819946
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020 _a0471465216
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020 _a9780471465218
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024 7 _a10.1002/0471221279
_2doi
029 1 _aAU@
_b000024297745
029 1 _aDEBSZ
_b43078337X
029 1 _aNZ1
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029 1 _aDEBBG
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035 _a(OCoLC)53219638
_z(OCoLC)85819946
_z(OCoLC)156809433
_z(OCoLC)647772312
_z(OCoLC)839280025
_z(OCoLC)961653397
_z(OCoLC)962674444
050 4 _aTJ214.5
_b.B37 2001eb
072 7 _aTEC
_x064000
_2bisacsh
082 0 4 _a681/.2
_222
049 _aMAIN
100 1 _aBar-Shalom, Yaakov.
_96300
245 1 0 _aEstimation with applications to tracking and navigation /
_cby Yaakov Bar-Shalom, Xiao-Rong Li, Thiagalingam Kirubarajan.
264 1 _aNew York :
_bWiley,
_c2001.
300 _a1 online resource (xxiii, 558 pages) :
_billustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _a"A Wiley-Interscience publication."
504 _aIncludes bibliographical references and index.
505 0 _aBackground -- Scope of the Text -- Brief Review of Linear Algebra and Linear Systems -- Brief Review of Probability Theory -- Brief Review of Statistics -- Basic Concepts in Estimation -- The Problem of Parameter Estimation -- Maximum Likelihood and Maximum a Posteriori Estimators -- Least Squares and Minimum Mean Square Error Estimation -- Unbiased Estimators -- The Variation and MSE of an Estimator -- Linear Estimation in Static Systems -- Estimation of Gaussian Random Vectors -- Linear Minimum Mean Square Error Estimation -- Least Squares Estimation -- Polynomial Fitting -- Goodness-of-Fit and Statistical Significance of Parameter Estimates -- Use of LS for a Nonlinear Problem: Bearings-Only Target Motion Analysis -- Linear Dynamic Systems with Random Inputs -- Continuous-Time Linear Stochastic Dynamic Systems -- Discrete-Time Linear Stochastic Dynamic Systems -- State Estimation in Discrete-Time Linear Dynamic Systems -- Linear Estimation in Dynamic Systems: The Kalman Filter -- Example of a Filter -- Consistency of State Estimators -- Initialization of State Estimators -- Sensitivity -- Estimation for Kinematic Models -- Discretized Continuous-Time Kinematic Models -- Direct Discrete-Time Kinematic Models -- Explicit Filters for Noiseless Kinematic Models -- Steady-State Filters for Noisy Kinematic Models -- Computation Aspects of Estimation -- The Information Filter -- Sequential Processing of Measurements -- Square-Root Filtering -- Extensions of Discrete-Time Linear Estimation -- Autocorrelated Process Noise -- Cross-Correlated Measurement and Process Noise -- Autocorrelated Measurement Noise -- Prediction -- Smoothing -- Continuous-Time Linear State Estimation -- The Continuous-Time Linear State Estimation Filter -- Prediction and the Continuous-Discrete Filter -- Duality of Estimation and Control -- The Wiener-Hopf Problem -- State Estimation for Nonlinear Dynamic Systems -- Estimation in Nonlinear Stochastic Systems -- The Extended Kalman Filter -- Error Compensation in Linearized Filters -- Some Error Reduction Methods -- Maximum a Posteriori Trajectory Estimation via Dynamic Programming -- Nonlinear Continuous-Discrete Filter -- Adaptive Estimation and Maneuvering Targets -- Adjustable Level Process Noise -- Input Estimation -- The Variable State Dimension Approach -- A Comparison of Adaptive Estimation Methods for Maneuvering Targets -- The Multiple Model Approach -- Design of an IMM Estimator for ATC Tracking -- When Is an IMM Estimator Needed? -- Use of EKF for Simultaneous State and Parameter Estimation -- Introduction to Navigation Applications -- Complementary Filtering for Navigation -- Inertial Navigation Systems -- Models for Inertial Navigation Systems -- The Global Positioning System (GPS) -- GPS Positioning -- The Accuracy of GPS Positioning -- State-Space Models for GPS -- Example: GPS Navigation with IMM Estimator -- Integrated Navigation.
520 _aExpert coverage of the design and implementation of state estimation algorithms for tracking and navigation. Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on.
588 0 _aPrint version record.
650 0 _aMotion control devices.
_95323
650 0 _aRemote control.
_96301
650 0 _aTelecommunication systems.
_96302
650 0 _aRobots
_xControl systems.
_93388
650 0 _aProcess control.
_95430
650 0 _aEstimation theory.
_96303
650 7 _aTECHNOLOGY & ENGINEERING
_xSensors.
_2bisacsh
_96304
650 7 _aEstimation theory.
_2fast
_0(OCoLC)fst00915531
_96303
650 7 _aMotion control devices.
_2fast
_0(OCoLC)fst01027082
_95323
650 7 _aProcess control.
_2fast
_0(OCoLC)fst01078020
_95430
650 7 _aRemote control.
_2fast
_0(OCoLC)fst01094465
_96301
650 7 _aRobots
_xControl systems.
_2fast
_0(OCoLC)fst01099044
_93388
650 7 _aTelecommunication systems.
_2fast
_0(OCoLC)fst01146038
_96302
653 _aControl systems technology.
655 4 _aElectronic books.
_93294
700 1 _aLi, Xiao-Rong.
_96305
700 1 _aKirubarajan, Thiagalingam.
_96306
776 0 8 _iPrint version:
_aBar-Shalom, Yaakov.
_tEstimation with applications to tracking and navigation.
_dNew York : Wiley, 2001
_z047141655X
_w(DLC) 2001022366
_w(OCoLC)46366251
856 4 0 _uhttps://doi.org/10.1002/0471221279
_zWiley Online Library
942 _cETB
994 _a92
_bDG1
999 _c81865
_d81865