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
    Course Adaptive Signal Processing
    Coordinating Unit School of Electrical & Electronic Engineering
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Assumed Knowledge Linear systems (discrete & continuous), linear algebra, probability theory, Fourier & Z transforms & MATLAB
    Course Description 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
    Email: lang.white@adelaide.edu.au
    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.
    University Graduate Attributes

    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
    Career and leadership readiness
    • 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    

    A. Poularikas, Z. Ramadan, Adaptive Filtering Primer with MATLAB®     
    S. Haykin Adaptive Filter Theory    
    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:
    https://myuni.adelaide.edu.au/webapps/login

    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.
    Workload

    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:
    http://eleceng.adelaide.edu.au/current-students/postgraduate/


    Course Grading

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

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    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
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.