ECE 4250

ECE 4250

Course information provided by the Courses of Study 2022-2023.

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

Outcomes
  • 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.

View Enrollment Information

Syllabi:
  •   Regular Academic Session.  Combined with: ECE 5250

  • 4 Credits Graded

  • 17054 ECE 4250   LEC 001

  • Instruction Mode: In Person