Previous edition published under the title: Lessons in digital estimation theory.
Includes bibliographical references (pages 542-552) and index.
Introduction, coverage, philosophy, and computation -- The linear model -- Least-squares estimation : batch processing -- Least-squares estimation : singular-value decomposition -- Least-squares estimation : recursive processing -- Small-sample properties of estimators -- Large-sample properties of estimators -- Properties of least-squares estimators -- Best linear unbiased estimation -- Likelihood -- Maximum-likelihood estimation -- Multivariate Gaussian random variables -- Mean-squared estimation of random parameters -- Maximum a posteriori estimation of random parameters -- Elements of discrete-time Gauss-Markov random sequences -- State estimation : prediction -- State estimation : filtering (the Kalman filter) -- State estimation : filtering examples -- State estimation : steady-state Kalman filter and its relationship to a digital Wiener filter -- State estimation : smoothing -- State estimation : smoothing (general results) -- State estimation for the not-so-basic state-variable model -- Linearization and discretization of nonlinear systems -- Iterated least squares and extended Kalman filtering -- Maximum-likelihood state and parameter estimation -- Kalman-Bucy filtering.