CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Digital Signal Processing
Prerequisites: ECE 4270
Catalog DescriptionBasic theory of adaptive filter design and implementation.
Steepest descent, LMS algorithm, nonlinear adaptive filters, and
neural networks. Analysis of performance and applications.
Textbook(s)Fundamentals of Adaptive Filtering
Student OutcomesIn the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.
1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. ( Not Applicable ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3. ( Not Applicable ) An ability to communicate effectively with a range of audiences
4. ( Not Applicable ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5. ( Not Applicable ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Strategic Performance Indicators (SPIs)
Background (1 week)
Review of Discrete-time random processes
FIR Wiener filters (1 week)
Derivation of the Wiener-Hopf equations
Principle of orthogonality
Problems and applications
Solving the Wiener-Hopf equations.
The Discrete Kalman Filter (1 week)
Gradient-based adaptive filters (4 weeks)
The LMS algorithm
Variations on the LMS algorithm
Examples and comparison of techniques
Gradient Adaptive Lattice Filter (0.5 weeks)
Recursive least squares (1.5 weeks)
Lattice filters - optional
Performace of the RLS algorithm
Tracking of time-varying systems (0.5 weeks) - Chapter 16 of Haykin
Adaptive IIR filters (1.5 weeks)
Fientuch and Horvath algorithms
HARF and SHARF
Examples and applications
Nonlinear adaptive filters (3 weeks) - Chapters 18-20 of Haykin
Order statistic adaptive filters and Volterra systems
Blind deconvolution - decision directed feedback
Back propagation learning
Radial basis function networks
Other Applications - optional
Adaptive line enhancement
Adaptive spectrum estimation, frequency tracking
Adaptive signal modeling