国外名校最新教材精选:线性估计(影印版)
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作者托马斯·凯拉斯(THomas Kailath)、阿里·H·赛义德(Ali H.Sayed)、巴巴克·哈斯比(Babak Hassibi) 著
出版社西安交通大学出版社
出版时间2008-12
版次1
装帧平装
上书时间2024-08-20
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图书标准信息
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作者
托马斯·凯拉斯(THomas Kailath)、阿里·H·赛义德(Ali H.Sayed)、巴巴克·哈斯比(Babak Hassibi) 著
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出版社
西安交通大学出版社
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出版时间
2008-12
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版次
1
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ISBN
9787560529493
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定价
98.00元
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装帧
平装
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开本
16开
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纸张
胶版纸
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页数
854页
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字数
1016千字
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正文语种
简体中文,英语
- 【内容简介】
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《线性估计(影印版)》主要介绍状态空间模型的有限维线性系统的估计问题,涵盖了目前我们熟知的维纳滤波和卡尔曼滤波这一领域的许多方面。《线性估计(影印版)》的三个独特之处是:’第一。将几何学的观点渗透于分析中;第二。侧重于将许多算法用平方根/阵列的形式给出;第三。强调了在解决自适应滤波、估计和控制这些相关问题时的等价性和对偶性概念。《线性估计(影印版)》由17章正文和7章附录构成。按内容可分为以下几个专题:
概论和基础知识(1—5章)
平稳过程估计(6书章)
非平稳过程估计(9—10章)
快速阵列算法(11—13章)
连续时间估计(16章)
高级专题(14,15,17章)
- 【作者简介】
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ThomasKailath博士,美国斯坦福大学教授,世界著名的控制与系统科学专家,美国科学院和工程院院士,第三世界科学院院士和印度工程院院士,IEEE会士(Fellow)。他的研究兴趣涉及信息理论、通信系统、计算、控制、线性系统、统计信号处理、大规模集成电路等,也是名著《线性系统理论》(LinearSystemTheory,Springer-Verla9,1991)的作者。ThomasKailath教授在多个研究领域做出了深远的贡献,并在1991年获得了IEEE信号处理分会的最高分会奖,在2000年获得了IEEE信息理论分会的Shannon奖。同时,ThomasKailath教授也是一名杰出的教育学者,他指导的博士生和博士后学者中许多人已在各自的研究领域做出了杰出的贡献。
AliH.Sayed博士,现为美国加州大学洛杉矶分校(UCLA)电气工程教授。IEEE会士。他的研究兴趣是自适应滤波、统计信号处理和估计算法等。
BabakHassibi博士,现为美国加州理工学院电气工程教授,1998-2000年曾在美国贝尔实验室工作。他的研究兴趣是通信、信号处理和控制等。
- 【目录】
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Preface
Symbols
1OVERVIEW
1.1TheAsymptoticObserver
1.2TheOptimumTransientObserver
1.2.1TheMean-Square-ErrorCriterion
1.2.2MinimizationviaCompletionofSquares
1.2.3TheOptimumTransientObserver
1.2.4TheKalmanFilter
1.3ComingAttractions
1.3.1SmoothedEstimators
1.3.2ExtensionstoTime-VariantModels
1.3.3FastAlgorithmsforTime-InvariantSystems
1.3.4NumericalIssues
1.3.5ArrayAlgorithms
1.3.6OtherTopics
1.4TheInnovationsProcess
1.4.1WhitenessoftheInnovationsProcess
1.4.2InnovationsRepresentations
1.4.3CanonicalCovarianceFactorization
1.4.4ExploitingState-SpaceStructureforMatrixProblems
1.5Steady-StateBehavior
1.5.1AppropriateSolutionsoftheDARE
1.5.2WienerFilters
1.5.3ConvergenceResults
1.6SeveralRelatedProblems
1.6.1AdaptiveRL$Fdtering
1.6.2LinearQuadraticControl
1.6.3HooEstimation
1.6.4HooAdaptiveFdtering
1.6.5HooControl
1.6.6LinearAlgebraandMatrixTheory
1.7Complements
Problems
2DETERMINISTICLEAST-SQUARESPROBLEMS
2.1TheDeterministicLeast-SquaresCriterion
2.2TheClassicalSolutions
2.2.1TheNormalEquations
2.2.2WeightedLeast-SquaresProblems
2.2.3StatisticalAssumptionsontheNoise
2.3AGeometricFormulation:TheOrthogonalityCondition
2.3.1TheProjectionTheoreminInnerProductSpaces
2.3.2GeometricInsights
2.3.3ProjectionMatrices
2.3.4AnApplication:Order-ReeursiveLeast-Squares
2.4RegularizedLeast-SquaresProblems
2.5AnArrayAlgorithm:TheORMethod
2.6UpdatingLeast-SquaresSolutions:RLSAlgorithms
2.6.1TheRLSAlgorithm
2.6.2AnArrayAlgorithmforRLS
2.7DowndatingLeast-SquaresSolutions
2.8SomeVariationsofLeast-SquaresProblems
2.8.1TheTotalLeast-SquaresCriterion
2.8.2CriteriawithBoundsonDataUncertainties
2.9Complements
Problems
2.AOnSystemsofLinearEquations
3STOCHASTICLEAST-SQUARESPROBLEMS
3.1TheProblemofStochasticEstimation
3.2LinearLeast-Mean-SquaresEstimators
3.2.1TheFundamentalEquations
3.2.2StochasticInterpretationofTriangularFactorization
3.2.3SingularDataCovarianceMatrices
3.2.4Nonzero-MeanValuesandCentering
3.2.5EstimatorsforComplex-ValuedRandomVariables
3.3AGeometricFormulation
3.3.1TheOrthogonalityCondition
3.3.2Examples
3.4LinearModels
3.4.1InformationFormsWhenRx>0andRv>0
3.4.2TheGauss-MarkovTheorem
3.4.3CombiningEstimators
3.5EquivalencetoDeterministicLeast-Squares
3.6Complements
Problems
3.7Least-Mean-SquaresEstimation
3.8GaussianRandomVariables
3.9OptimalEstimationforGaussianVariables
4THEINNOVATIONSPROCESS
4.1EstimationofStochasticProcesses
4.1.1TheFixedIntervalSmoothingProblem
4.1.2TheCausalFdteringProblem
4.1.3TheWiener-HopfTechnique
4.1.4ANoteonTerminology——VectorsandGramians
4.2TheInnovationsProcess
4.2.1AGeometricApproach
4.2.2AnAlgebraicApproach
4.2.3TheModifiedGram-SchmidtProcedure
4.2.4EstimationGiventheInnovationsProcess
4.2.5TheFilteringProblemviatheInnovationsApproach
4.2.6ComputationalIssues
4.3InnovationsApproachtoDeterministicLeast-SquaresProblems
4.4TheExponentiallyCorrelatedProcess
4.4.1TriangularFactorizationofRy
4.4.2FindingL-1andtheInnovations
4.4.3InnovationsviatheGram-SchmidtProcedures
4.5Complements
Problems
4.6LinearSpaces,Modules,andGramians
5STATE-SPACEMODELS
5.1TheExponentiallyCorrelatedProcess
5.1.1FiniteIntervalProblems;InitialConditionsforStationarity
5.1.2InnovationsfromtheProcessModel
5.2GoingBeyondtheStationaryCase
5.2.1StationaryProcesses
5.2.2NonstationaryProcesses
5.3Higher-OrderProcessesandState-SpaceModels
5.3.1AutoregressiveProcesses
5.3.2HandlingInitialConditions
5.3.3State-SpaceDescriptions
5.3.4TheStandardState-SpaceModel
5.3.5ExamplesofOtherState-SpaceModels
5.4Wide-SeuseMarkovProcesses
5.4.1ForwardsMarkovianModels
5.4.2BackwardsMarkovianModels
5.4.3BackwardsModelsfromForwardsModels
5.4.4MarkovianRepresentationsandtheStandardModel
5.5Complements
Problems
5.6SomeGlobalFormulas
6INNOVATIONSFORSTATIONARYPROCESSES
6.1InnovationsviaSpectralFactorization
6.1.1StationaryProcesses
6.1.2GeneratingFunctionsandz-Spectra
6.2SignalsandSystems
6.2.1Thez-Transform
6.2.2LinearTime-InvariantSystems
6.2.3Causal,Anticausal,andMinimum-PhaseSystems
6.3StationaryRandomProcesses
6.3.1Propertiesofthez-Spectrum
6.3.2LinearOperationsonStationaryStochasticProcesses
6.4CanonicalSpectralFactorization
6.5ScalarRationalz-Spectra
6.6Vector-ValuedStationaryProcesses
6.7Complements
Problems
6.8Continuous-TimeSystemsandProcesses
7WIENERTHEORYFORSCALARPROCESSES
7.1Continuous-TimeWienerSmoothing
7.1.1TheGeometricFormulation
7.1.2SolutionviaFourierTransforms
7.1.3TheMinimumMean-SquareError
7.1.4FilteringSignalsoutofNoisyMeasurements
7.1.5ComparisonwiththeIdealFilter
7.2TheContinuous-TimeWiener-HopfEquation
7.3Discrete-TrineProblems
7.3.1TheDiscrete-TrineWienerSmoother
7.3.2TheDiscrete-TrineWiener-HopfEquation
7.4TheDiscrete-TrineWiener-HopfTechnique
7.5CausalPartsViaPartialFractions
7.6ImportantSpecialCasesandExamples
7.6.1PurePrediction
7.6.2AdditiveWhiteNoise
……
8RECURSIVEWIENERFILTERING
9THEKALMANFILTER
10SMOOTHEDESTIMATORS
11FASTALGORITHMS
12ARRAYALGORITHMS
13FASTARRAYALGORITHMS
14ASYMPTOTICBEHAVIOR
15DUALITYANDEQUIVALENCEINESTIMATIONANDCONTROL
16CONTINUOUS-TIMESTATE-SPACEESTIMATION
17ASCATTERINGTHEORYAPPROACH
AUSEFULMATRIXRESULTS
BUNITARYANDJ-UNITARYTRANSFORMATIONS
CSOMESYSTEMTHEORYCONCEPTS
DLYAPUNOVEQUATIONS
EALGEBRAICRICCATIEQUATIONS
FDISPLACEMENTSTRUCTURE
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