Introduction to Random Signals, Estimation Theory, and Kalman Filtering

M. Sami Fadali earned a B.S. in Electrical Engineering from Cairo University in 1974, an M.S. from the Control Systems Center, UMIST, England, in 1977, and a Ph.D. from the University of Wyoming in 1980. He was an Assistant Professor of Electrical Engineering at the University of King Abdul Aziz in Jeddah, Saudi Arabia from 1981-1983. From 1983-85, he was a Postdoctoral Fellow at Colorado State University. In 1985, he joined the Electrical Engineering Department at the University of Nevada, Reno, where he is currently Professor of Electrical Engineering. In 1994, he was a Visiting Professor at Oakland University and GM Research and Development Laboratories. He spent the summer of 2000 as a Senior Engineer at TRW, San Bernardino. His research interests are in fuzzy logic stability and control, state estimation and fault detection, and applications to power systems, renewable energy, and physiological systems.

Review of Probability Theory.- Random Variables.- Random Signals (autocorrelation, power spectral density).- Response of Linear Systems to Random Inputs (continuous, discrete).- Estimation and Estimator Properties (small sample and large sample properties of estimators, CRLB).- Least Square Estimation Likelihood (likelihood function, detection).- Maximum Likelihood Estimation.- Minimum Mean-Square Error Estimation (Kalman Filter, information filter, filter stability).- Generalizing the Basic Kalman Filter (colored noise, correlated noise, reduced-order estimator, Schmidt Kalman filter sequential computation).- Prediction and Smoothing.- Nonlinear Filtering (Extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, particle filter).- The Expectation Maximization Algorithm.- Markov Models.