This course introduces the fundamental tools for the estimation, detection, and prediction of discrete-time random signals
INTRODUCTION and FOUNDATIONS:
· Detection and estimation
· Calculus, probability, and linear systems
PART 1: Stochastic processes
· Introduction and examples
· First and second order statistics
· Stationarity and ergodicity
· Power spectral density
PART 2: Estimation theory
· Parameter estimation
· Bayesian estimation
· Time series
· Filtering, prediction and smoothing
· Power spectral density estimation
PART 3: Detection theory
· Introduction and examples
· Performance metrics for detectors
· Detector design
· Sequential detection