国外电子信息类系列教材：统计与自适应信号处理（英文改编版）

图书标准信息

• 作者 [美]Dimitris、[美]Vinay、[美]Stephen M.Kogon 著
• 出版社 西安电子科技大学出版社
• 出版时间 2012-08
• 版次 1
• ISBN 9787560628486
• 定价 56.00元
• 装帧 平装
• 开本 16开
• 纸张 胶版纸
• 页数 382页

【内容简介】

　　《国外电子信息类系列教材：统计与自适应信号处理（英文改编版）》介绍了统计与自适应信号处理的基本概念和应用，包括随机序列分析、谱估计以及自适应滤波等内容。本书可作为电子、通信、自动化、电机、生物医学和机械工程等专业研究生作为教材或教学参考书，也可作为广大工程技术人员的自学读本或参考用书。

【作者简介】

　　DimitrisG.Manolakis：于希腊雅典大学获得物理学士学位和电气工程博士学位，现任美国麻省林肯实验室研究员；曾在Riveride研究所任主任研究员，并曾在雅典大学、美国东北大学、波士顿学院、沃切斯特理工学院任教。
　　VinayK.Ingle：于伦斯勒理工学院获得电气和计算机工程的博士学位，曾在多所大学讲授过信号处理课程，具有丰富的研究经历；1981年加入美国东北大学，目前在电气工程和计算机系任职。
　　StephenM.Kogon：于佐治亚理工学院获得电气工程博士学位，现任美国麻省林肯实验室研究员；曾就职于Raytheon公司、波士顿大学和佐治亚技术研究所。

【目录】

CHAPTER1Introduction
1.1RandomSignals
1.2SpectralEstimation
1.3SignalModeling
1.4AdaptiveFiltering
1.4.1ApplicatiorofAdaptiveFilter
1.4.2FeaturesofAdaptiveFilter
1.5OrganizationoftheBook
CHAPTER2RandomSequences
2.1Discrete-TimeStochasticProcesses
2.1.1DescriptionUsingProbabilityFunctior
2.1.2Second-OrderStatisticalDescription
2.1.3Stationarity
2.1.4Ergodicity
2.1.5RandomSignalVariability
2.1.6Frequency-DomainDescriptionofStationaryProcesses
2.2LinearSystemswithStationaryRandomInputs
2.2.1Time-DomainAnalysis
2.2.2Frequency-DomainAnalysis
2.2.3RandomSignalMemory
2.2.4GeneralCorrelationMatrices
2.2.5CorrelationMatricesfromRandomProcesses
2.3InnovatiorRepresentationofRandomVector
2.4PrinciplesofEstimationTheory
2.4.1PropertiesofEstimator
2.4.2EstimationofMean
2.4.3EstimationofVariance
2.5Summary
Problems
CHAPTER3LinearSignalModels
3.1Introduction
3.1.1LinearNonparametricSignalModels
3.1.2ParametricPole-ZeroSignalModels
3.1.3MixedProcessesandWoldDecomposition
3.2All-PoleModels
3.2.1ModelProperties
3.2.2All-PoleModelingandLinearPrediction
3.2.3AutoregressiveModels
3.2.4Lower-OrderModels
3.3All-ZeroModels
3.3.1ModelProperties
3.3.2Moving-AverageModels
3.3.3Lower-OrderModels
3.4Pole-ZeroModels
3.4.1ModelProperties
3.4.2AutoregressiveMoving-AverageModels
3.4.3TheFirt-OrderPole-ZeroModel：PZ（1，1）
3.4.4SummaryandDualities
3.5Summary
Problems
CHAPTER4NonparametricPowerSpectrumEstimation
4.1SpectralAnalysisofDeterministicSignals
4.1.1EffectofSignalSampling
4.1.2Windowing，PeriodicExterion，andExtrapolation
4.1.3EffectofSpectrumSampling
4.1.4EffectsofWindowing：LeakageandLossofResolution
4.1.5Summary
4.2EstimationoftheAutocorrelationofStationaryRandomSignals
4.3EstimationofthePowerSpectrumofStationaryRandomSignals
4.3.1PowerSpectrumEstimationUsingthePeriodogram
4.3.2PowerSpectrumEstimationbySmoothingaSinglePeriodogram——TheBlackman-TukeyMethod
4.3.3PowerSpectrumEstimationbyAveragingMultiplePeriodograms——TheWelch-BartlettMethod
4.3.4SomePracticalCorideratiorandExamples
4.4MultitaperPowerSpectrumEstimation
4.5Summary
Problems
CHAPTER5OptimumLinearFilter
5.1OptimumSignalEstimation
5.2LinearMeanSquareErrorEstimation
5.2.1ErrorPerformanceSurface
5.2.2DerivationoftheLinearMMSEEstimator
5.2.3Principal-ComponentAnalysisoftheOptimumLinearEstimator
5.2.4GeometricInterpretatiorandthePrincipleofOrthogonality
5.2.5SummaryandFurtherProperties
5.3OptimumFiniteImpulseResporeFilter
5.3.1DesignandProperties
5.3.2OptimumFIRFilterforStationaryProcesses
5.3.3Frequency-DomainInterpretatior
5.4LinearPrediction
5.4.1LinearSignalEstimation
5.4.2ForwardLinearPrediction
5.4.3BackwardLinearPrediction
5.4.4StationaryProcesses
5.4.5Properties
5.5OptimumInfiniteImpulseResporeFilter
5.5.1NoncausalIIRFilter
5.5.2CausalIIRFilter
5.5.3FilteringofAdditiveNoise
5.5.4LinearPredictionUsingtheInfinitePast——Whitening
5.6InvereFilteringandDeconvolution
5.7Summary
Problems
CHAPTER6AlgorthmsandStructuresforOptimumLinearFilter
6.1FundamentalsofOrder-RecuriveAlgorithms
6.1.1MatrixPartitioningandOptimumNesting.
6.1.2InverionofPartitionedHermitianMatrices
6.1.3LevironRecurionfortheOptimumEstimator
6.1.4Order-RecuriveComputationoftheLDLHDecomposition
6.1.5Order-RecuriveComputationoftheOptimumEstimate
6.2InterpretatiorofAlgorithmicQuantities
6.2.1InnovatiorandBackwardPrediction
6.2.2PartialCorrelation
6.2.3OrderDecompositionoftheOptimumEstimate
6.2.4Gram-SchmidtOrthogonalization
6.3Order-RecuriveAlgorithmsforOptimumFIRFilter
6.3.1Order-RecuriveComputationoftheOptimumFilter
6.3.2Lattice-LadderStructure
6.3.3SimplificatiorforStationaryStochasticProcesses
6.4AlgorithmsofLevironandLeviron-Durbin
6.5LatticeStructuresforOptimumFirFilterAndPredictor
6.5.1Lattice-LadderStructures
6.5.2SomePropertiesandInterpretatior
6.5.3ParameterConverior
6.6Summary
Problems
CHAPTER7Least-SquaresFilteringandPrediction
7.1ThePrincipleofLeastSquares
7.2LinearLeast-SquaresErrorEstimation
7.2.1DerivationoftheNormalEquatior
7.2.2StatisticalPropertiesofLeast-SquaresEstimater
7.3Least-SquaresFIRFilter
7.4LinearLeast-SquaresSignalEstimation
7.4.1SignalEstimationandLinearPrediction
7.4.2CombinedForwardandBackwardLinearPrediction（FBLP）
7.4.3NarrowbandInterferenceCancelation
7.5LSComputatiorUsingtheNormalEquatior
7.5.1LinearLSEEstimation
7.5.2LSEFIRFilteringandPrediction
7.6Summary
Problems
CHAPTER8SignalModelingandParametricSpectralEstimation
8.1TheModelingProcess：TheoryandPractice
8.2EstimationofAll-PoleModels
8.2.1DirectStructures
8.2.2LatticeStructures
8.2.3MaximumEntropyMethod
8.2.4ExcitatiorwithLineSpectra
8.3EstimationOfPole-ZeroModels
8.3.1KnownExcitation
8.3.2UnknownExcitation
8.4Applicatior
8.4.1SpectralEstimation
8.4.2SpeechModeling
8.5HarmonicModelsandFrequencyEstimationTechniques
8.5.1HarmonicModel
8.5.2PisarenkoHarmonicDecomposition
8.5.3MUSICAlgorithm
8.5.4Minimum-NormMethod
8.5.5ESPRITAlgorithm
8.6Summary
Problems
CHAPTER9AdaptiveFilter
9.1TypicalApplicatiorofAdaptiveFilter
9.1.1EchoCancelationinCommunicatior
9.1.2LinearPredictiveCoding
9.1.3NoiseCancelation
9.2PrinciplesofAdaptiveFilter
9.2.1FeaturesofAdaptiveFilter
9.2.2OptimumverusAdaptiveFilter
9.2.3StabilityandSteady-StatePerformanceofAdaptiveFilter
9.2.4SomePracticalCorideratior
9.3MethodofSteepestDescent
9.4Least-Mean-SquareAdaptiveFilter
9.4.1Derivation
9.4.2AdaptationinaStationarySOE
9.4.3SummaryandDesignGuidelines
9.4.4ApplicatioroftheLMSAlgorithm
9.4.5SomePracticalCorideratior
9.5RecuriveLeast-SquaresAdaptiveFilter
9.5.1LSAdaptiveFilter
9.5.2ConventionalRecuriveLeast-SquaresAlgorithm
9.5.3SomePracticalCorideratior
9.5.4ConvergenceandPerformanceAnalysis
9.6FastRLSAlgorithmsforFIRFiltering
9.6.1FastFixed-OrderRLSFIRFilter
9.6.2RLSLattice-LadderFilter
9.6.3RLSLattice-LadderFilterUsingErrorFeedbackUpdatings
9.7TrackingPerformanceofAdaptiveAlgorithms
9.7.1ApproachesforNortationarySOE
9.7.2PreliminariesinPerformanceAnalysis
9.7.3LMSAlgorithm
9.7.4RLSAlgorithmwithExponentialForgetting
9.7.5ComparisonofTrackingPerformance
9.8Summary
Problems