ECE 6202
Transcript Abbreviation:
Stoch Sig Proc
Course Description:
Spectrum estimation, array processing, and adaptive filtering.
Course Levels:
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites
(N/A)
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 800 or 801.01.
Course Goals / Objectives:
Apply filtering techniques to the design and analysis of sensor arrays
Learn the foundations of adaptive filter theory: transient and steady-state behaviors of adaptive filtering algorithms
Develop facility with MATLAB as a tool for explanatory analysis and algorithm implementation in statistical signal processing
Apply vector space methods to stochastic signal processing problems
Learn fundamental bounds on estimation performance, with application to harmonic retrieval
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Review of random processes | 3.0 | 0.0 | 0.0 | 0 |
| Classical methods for spectrum estimation | 4.0 | 0.0 | 0.0 | 0 |
| Parametric techniques for spectrum estimation | 5.0 | 0.0 | 0.0 | 0 |
| Filtering and prediction | 3.0 | 0.0 | 0.0 | 0 |
| Harmonic retrieval and fundamental bounds | 4.0 | 0.0 | 0.0 | 0 |
| Array processing | 5.0 | 0.0 | 0.0 | 0 |
| LMS transient and steady-state behavior | 8.0 | 0.0 | 0.0 | 0 |
| LMS extensions, least-squares solutions and gemoetric interpretations | 5.0 | 0.0 | 0.0 | 0 |
| Recursive least squares: transient and steady-state behavior | 3.0 | 0.0 | 0.0 | 0 |
| Total | 40 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework and MATLAB based computer exercises | 50% |
| One midterm exam | 25% |
| Final exam | 25% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Statistical Digital Signal Processing and Modeling | M. Hayes | |
| Course Notes | Phil Schniter |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes
(N/A)
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)