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nahi n estimation theory applications 1976

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Textbook in PDF format A Mahtematical Survey Introduction Matrix Algebra and Quadratic Forms Matrices Matrix Operations Eigenvalues and Eigenfunctions Matrix Rank and Linear Dependence Inner and Outer Vector Products Symmetric Matrices Matrix Diagonalization Quadratic Forms Matrix Functions Scalar Functions of a Square Matrix Gradient Vector The Cayley-Hamilton Theorem Differential and Difference Equations General Remarks Linearity Autonomous Systems Homogeneous Solution The Inhomogeneous Solution Linear Time-Invariant Systems Impulse Response Evaluation of Transition Matrix Linear Difference Equations Elements of Random Processes Events and Probabilities Joint, Marginal, and Conditional Probabilities Statistical Independence Random Variable Moments Random Processes The Statistics of Processes Mean, Correlation, Covariance Orthogonal Processes Stationary Processes Ergodic Processes Functions of Random Variables and Processes Linear Continuous Systems Linear Discrete Systems Gaussian (Normal) Processes Markov Processes Fourier Transform and Spectral Density Fourier Transform Spectral Density Linear Systems Calculus of Variations Derivation of a Necessary Condition A Special Case Quadratic Functionals Problems Selected Readings Estimation Theory Concepts and Criteria Introduction Criteria of Estimation An Example Bayes Minimum Quadratic Cost Estimate Gaussian Processes Minimization of Average Nonquadratic Cost Biased and Unbiased Estimates Problems Selected Readings Modelling of Stochastic Processes Introduction Discrete Estimation, Problem Statement Principle of Orthogonality, Discrete Case Discrete Estimator (Kalman) Continuous Estimation, Problem Statement Principle of Orthogonality, Continuous Case Continuous Optimal Estimator (Kalman-Bucy) The Error Covariance Equation Continuous Estimator, Examples Optimal Prediction, Continuous Case Optimal Prediction, Discrete Case The Problem of Missing Data Optimal Interpolation, Discrete Case Discrete Filtering Examples A Special Case Optimal Interpolation, Continuous Case Problems Selected Readings Introduction Continuous Time-Invariant Estimator, Problem Statement Time-invariant Linear Estimators Introduction Differential Equation Model of Random Processes Difference Equation Model of Random Sequences Discussion of Differential and Difference Equation Models Parameter Representation by the Model Linear Models Observability Problems Selected Readings Linear Estimators Derivation of Continuous Estimator An Alternative Derivation Estimation Error Examples Discrete Estimator, Problem Statement Derivation of Discrete Filter Problems Selected Readings Additional Topics on Linear Estimators Introduction Measurements Containing Colored Noise Partially or Completely Noise-Free Measurements Effect of Inaccurate ^ Pr/on Model Parameters Linear Estimators with Constant Memory Numerical Solution of the Error Covariance Equation Problems Selected Readings Nonlinear Estimation Introduction Problem Statement Method of Linearization Evolution of Probability Density Function Nonlinear Recursive Estimation, The Direct Method Problems Selected Readings Maximum Likelihood and Least Square Estimation Introduction Maximum Likelihood Estimation Connection with Least Square Estimation Recursive Least Square Estimation Connection with Kalman Filtering Curve Fitting Maximum Likelihood Estimation with Colored Observation Noise Least Square Estimates of Solutions of Nonlinear Dynamic SystemsContents Method of Moments Problems Selected Readings System Identification Introduction Linear Constant-Coefficient Systems Optimal Estimation of Impulse Response Parameter Estimation, Linear Systems Parameter Estimation, Nonlinear Systems Problems Selected Readings Signal of Known Form in the Presence of Noise Introduction Signals with Partially Known Form Signals with Completely Known Form Maximization of Signal-to-Noise Ratio The Matched Filter Problems Selected Readings Cramer-Rao Lower Bound and its Applications Introduction Derivation of the Lower Bound Efficient Estimators Additive Gaussian Noise Signal Design for Optimal Estimation Problem Formulation (Gaussian Noise) Observation Linear in a: Observation a Nonlinear Function of a; Dependence of Input on the Estimation Parameter Problems Selected Readings Appendix Index

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nahi n estimation theory applications 1976