Digital Signal Processing and Statistical Inference

Catalog description

This course introduces discrete-time signal and system models in deterministic and stochastic settings and develops signal processing and statistical inference methodologies for real-time sensing and control applications. The course is intended for upper-level undergraduate and beginning graduate engineering students in engineering departments.The course covers both deterministic and stochastic techniques. Specific topics include time and frequency domain representation of signals and systems, state-space representation, feedback, stability, linear and nonlinear filtering, signal and state estimation and tracking, hypothesis testing, and signal detection. Applications in communications and control system design are integrated into the course material.