ECE 4250

Digital Signal Processing and Statistical Inference

Course Description

Course information provided by the Courses of Study 2024-2025.

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.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: MATH 1920 and MATH 2940, ECE 3100, or equivalent course that satisfies ECE probability requirements, ECE 2720 and ECE 3250, or equivalent courses.

• Be able to obtain impulse response from frequency and state-space models and vice versa. Be able to analyze system stability, reachability, and observability given a linear time-invariant state space model.
• Be able to design and implement state and observer-based feedback systems that stabilize an unstable system.
• Be able to understand stationary and wide-sense stationary models of discrete-time signal and the notion of power spectrum density of a wide-sense stationary process.
• Be able to solve signal estimation and detection problems under parametric and state-space models, including implementing Wiener and Kalman filtering techniques for estimation, and using matched filtering in signal detection.

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