## ELEC ENG 7015 - Adaptive Signal Processing

### North Terrace Campus - Semester 1 - 2016

Introductory and Preliminary material - Introduction to the concepts, key issues and motivating examples for adaptive filters; Discrete time linear systems and filters; Random variables and random processes, covariance matrices; Z transforms of stationary random processes. Optimum Linear Systems - Error surfaces and minimum mean square error; Optimum discrete time Wiener filter; Principle of orthogonality and canonical forms; Constrained optimisation; Method of steepest descent - convergence issues; Stochastic gradient descent LMS - convergence in the mean and mis-adjustment Case study. Least squares and recursive least squares. Linear Prediction - Forward and backward linear prediction; Levinson Durbin; Lattice filters.

• General Course Information
##### Course Details
Course Code ELEC ENG 7015 Adaptive Signal Processing School of Electrical & Electronic Engineering Semester 1 Postgraduate Coursework North Terrace Campus 3 Up to 3 hours per week Y Linear systems (discrete & continuous), linear algebra, probability theory, Fourier & Z transforms & MATLAB Introductory and Preliminary material - Introduction to the concepts, key issues and motivating examples for adaptive filters; Discrete time linear systems and filters; Random variables and random processes, covariance matrices; Z transforms of stationary random processes. Optimum Linear Systems - Error surfaces and minimum mean square error; Optimum discrete time Wiener filter; Principle of orthogonality and canonical forms; Constrained optimisation; Method of steepest descent - convergence issues; Stochastic gradient descent LMS - convergence in the mean and mis-adjustment Case study. Least squares and recursive least squares. Linear Prediction - Forward and backward linear prediction; Levinson Durbin; Lattice filters.
##### Course Staff

Course Coordinator: Professor Lang White

Course Coordinator and Lecturer: Professor Lang White
Office: IW2.21
Phone: 8313 5055
##### Course Timetable

The full timetable of all activities for this course can be accessed from Course Planner.

• Learning Outcomes
##### Course Learning Outcomes
After completion of this course, students will be able to:

1. Understand and derive the FIR Wiener filter for signals with known second order statistics.
2. Use and understand the LMS algorithm for iteratively estimating the Wiener filter weights.
3. Determine suitable LMS step size to trade off convergence time and misadjustment.
4. Derive and apply the RLS algorithm for iteratively estimating the Wiener filter weights.
5. Be familiar with the prediction filter formulation and applications
6. Solve the Wiener filter weights for the prediction filter using the Levinson-Durbin algorithm
7. Be familiar with the Lattice filter implementation of the prediction filter.
8. Derive the Lattice filter architecture from the Levinson-Durbin algorithm
9. Apply a modified LMS algorithm to the lattice structure to improve convergence times.
10. Use Matlab to implement the Wiener filter, Least Squares, LMS and RLS algorithms, and apply to selected applications.

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

University Graduate Attribute Course Learning Outcome(s)
Deep discipline knowledge
• informed and infused by cutting edge research, scaffolded throughout their program of studies
• acquired from personal interaction with research active educators, from year 1
• accredited or validated against national or international standards (for relevant programs)
all
Critical thinking and problem solving
• steeped in research methods and rigor
• based on empirical evidence and the scientific approach to knowledge development
• demonstrated through appropriate and relevant assessment
all
Teamwork and communication skills
• developed from, with, and via the SGDE
• honed through assessment and practice throughout the program of studies
• encouraged and valued in all aspects of learning
partial
• technology savvy
• professional and, where relevant, fully accredited
• forward thinking and well informed
• tested and validated by work based experiences
partial
Intercultural and ethical competency
• adept at operating in other cultures
• comfortable with different nationalities and social contexts
• able to determine and contribute to desirable social outcomes
• demonstrated by study abroad or with an understanding of indigenous knowledges
n/a
Self-awareness and emotional intelligence
• a capacity for self-reflection and a willingness to engage in self-appraisal
• open to objective and constructive feedback from supervisors and peers
• able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
all
• Learning Resources
##### Required Resources
A set of course notes, practice problems and other supporting materials will be available for downloading from the course web site.
##### Recommended Resources
Main References

C.W. Therrien Discrete Random Signals and Statistical Signal Processing
W.B. Davenport and W.L. Root An Introduction to the Theory of Random Signals and Noise
B Widrow and S.D. Stearns Adaptive Signal Processing

Supplementary References
S.T. Alexander Adaptive Signal Processing - Theory and Applications
V Solo and X Kong Adaptive Signal Processing Algorithms
R.A. Monzingo and T.W. Miller Introduction to Adaptive Arrays
F Hsu and A.A. Giordano Least Squares Signal Processing
S.J. Orfanidis Optimum Signal Processing
G.C. Goodwin and K.S. Sim Adaptive Filtering, Prediction and Control
M.L. Honig and D.G. Messerschmidtt Advanced Signal Processing
B.D.O. Anderson and J.B. Moore Optimal Filtering
C.F.N. Cowan and P.M. Grant Adaptive Filters
Y. Bar Shalom Tracking and Data Association
P A Regalia Adaptive IIR Filtering in Signal Processing and Control
L.H. Sibul (Ed) Adaptive Signal Processing
M. G. Bellanger Adaptive Digital Filters and Signal Analysis Marcel Dekker 1987
##### Online Learning
Extensive use will be made of the MyUni web site for this course:

Course notes, tutorial problems and solutions, laboratory exercises and practice problems will all be available for downloading from the web site.  Where the lecture theatre facilities permit, audio or video recordings of lectures will also be available for downloading.There will be two on-line quizzes to be completed.
• Learning & Teaching Activities
##### Learning & Teaching Modes
This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. Matlab based assignments are used to provide hands-on experience for students to reinforce the theoretical concepts encountered in lectures. Class tests provide formative assessment opportunities for students to gauge their progress and understanding.

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

 Activity No. Contact Hours Workload Hours Lectures 24 24 60 Tutorials 6 6 24 Assignments 2 50 In-class tests 2 2 20 Total 32 154
##### Learning Activities Summary
 Activity Session Week Topic Lecture 1-3 1 Review of random processes 4-5 2 Optimal FIR Wiener filter 6-8 3 Prediction error formulations 9-10 4 Lattice filters 11-13 5 LMS Algorithm 14-15 7 Adaptive Lattice Filters 16-17 8 Least Squares Estimation 18-20 9 RLS algorithm 21-22 11 Selected Topics in Adaptive Filtering 23-24 12 Revision Tutorial 1 2 Random Processes 2 4 Wiener filters 3 6 Lattice Filters 4 8 LMS Algorithm 5 10 RLS Algorithm 6 12 Selected topics in adaptive filtering In-class Tests 1 6 LMS Algorithm 2 10 RLS Algorithm
##### Specific Course Requirements
Not applicable
##### Small Group Discovery Experience
Not applicable.
• Assessment

The University's policy on Assessment for Coursework Programs is based on the following four principles:

1. Assessment must encourage and reinforce learning.
2. Assessment must enable robust and fair judgements about student performance.
3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
4. Assessment must maintain academic standards.

##### Assessment Summary
 Assessment Activity Type Weighting Due Date Learning Objective Addressed In class tests Summative and formative 40% Weeks 6,12 All Assignments Summative and formative 60% Weeks 7,13 All
##### Assessment Related Requirements
This subject has no final examination. There are no hurdle requirements.
##### Assessment Detail
Details of individual assessment tasks will be provided during the semester.
##### Submission
All written submissions to formative assessment activities are to be submitted to designated boxes within the School of Electrical & Electronic Engineering by 3:00pm on the specified dated and must be accompanied by a signed cover sheet. Copies of blank cover sheets are available from the School office in Ingkarni Wardli 3.26.No late submissions will be accepted. All formative assessments will have a two week turn-around time for provision of feedback to students.

Full details can be found at the School policies website:

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
FNS   Fail No Submission
F 1-49 Fail
P 50-64 Pass
C 65-74 Credit
D 75-84 Distinction
HD 85-100 High Distinction
CN   Continuing
NFE   No Formal Examination
RP   Result Pending

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through Access Adelaide.

• Student Feedback

The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

• Student Support
• Policies & Guidelines
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