Response of continuous and discrete time, linear and nonlinear systems to Gaussian and non-Gaussian random processes. Signal to noise power ratio computations (SNR) of systems. Introduction to signal detection theory. Optimal filtering (estimation) techniques of Wiener and Kalman to both open and closed loop systems.
The analysis of system response to stochastic signals and noise is fundamental for the understanding of advanced system analysis and synthesis.
Percentage of Course
|Linear System transformations on multivariate Gaussian processes and Brownian motion||10%|
|Narrowband Gaussian and Gaussian-derived processes, e.g. processes with Rayleigh and Rician densities||10%|
|Response of open and closed loop systems to stochastic inputs||10%|
|Response of nonlinear systems to stationary stochastic process||10%|
|Filtering, smoothing and prediction of stationary stochastic processes; Wiener and matched filtering.||20%|
|Hypothesis testing, maximum likelihood ratio decisions; detection of known signals in a noisy environment.||20%|
|Introduction to state estimation theory in discrete time, linear, scalar systems.||20%|