ELEC ENG 7002 - Kalman Filtering & Applications

North Terrace Campus - Semester 2 - 2015

The Kalman Filter: Stochastic state-variable systems; Optimality criteria for the estimation of state variables; The maximum-likelihood solution for independent Gaussian noise processes; The innovations sequence; The least-squares Kalman filter; Systems with correlated noise processes; Stochastic systems with time-invariant coefficients; The square-root algorithm; The extended Kalman filter, Adaptive system identification.

  • General Course Information
    Course Details
    Course Code ELEC ENG 7002
    Course Kalman Filtering & Applications
    Coordinating Unit School of Electrical & Electronic Engineering
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 42 hours
    Available for Study Abroad and Exchange Y
    Assumed Knowledge Linear algebra (matrices), probability theory, linear systems & MATLAB
    Course Description The Kalman Filter: Stochastic state-variable systems; Optimality criteria for the estimation of state variables; The maximum-likelihood solution for independent Gaussian noise processes; The innovations sequence; The least-squares Kalman filter; Systems with correlated noise processes; Stochastic systems with time-invariant coefficients; The square-root algorithm; The extended Kalman filter, Adaptive system identification.
    Course Staff

    Course Coordinator: Professor Peng Shi

    Email: peng.shi@adelaide.edu.au
    Phone: 8313 6424
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    At the completion of this course students will be able to:

    1. Have some fundamental knowledge of Kalman filtering
    2. Understand the properties and structure of Kalman filter
    3. Know how to design Kalman filter for simple practical cases
    4. Be familiar with basic target tracking theory and its 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)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1-3
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1-3
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 4
    Skills of a high order in interpersonal understanding, teamwork and communication. 4
    A proficiency in the appropriate use of contemporary technologies. 4
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1-4
    A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 2-3
    An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 3-4
  • Learning Resources
    Required Resources
    There are no required resources; lecture notes will be available on the MyUni website.
    Recommended Resources
    • A New Approach to Linear Filtering and Prediction Problems/ R. E. Kalman.
    • Stochastic Models, Estimation, and Control/ P. S. Maybeck.
    • An Introduction to the Kalman Filter/ G. Welch and G. Bishop
    • Kalman Filtering with Its Real-Time Applications/ C. K. Chui and G. Chen
    • Kalman Filtering: Theory and Application / edited by H.W. Sorenson.
    • Kalman Filtering Techniques for Radar Tracking / K.V. Ramachandra.
    • Optimal Filtering / B.D. O. Anderson, J.B. Moore.
    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, project requirements, course schedule, group list and a practice exam will all be available for downloading from the website.

    Tutorial solutions will NOT be available online

    Where the lecture theatre facilities permit, audio or video recordings of lectures will also be available for downloading.
  • 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.

    Practicals are used to provide hands-on experience for students to reinforce the theoretical concepts encountered in lectures.

    Continuous assessment activities provide the 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 Contact hours Workload hours
    Lecture 24 24 72
    Tutorials 8 8 12
    Assignment 50% 4 12
    Exam 50% 4 12
    TOTALS 40 108
    Learning Activities Summary
    Activity Sessions Topic
    Lecture


    1,2 Rudimentary Knowledge of Kalman Filter and Tracking: Matrix and Determinant Preliminaries; Probability Preliminaries; Least-Squares Preliminaries;
    3,4 Kalman Filter: An Elementary Approach; The Kalman Filter Model; Optimality Criterion; Prediction-Correction Formulation; Kalman Filtering Process;
    5 Orthogonal Projection and Kalman Filter: Orthogonality Characterization of Optimal Estimates; Innovations Sequences; Minimum Variance Estimate; Kalman Filtering Equations; Real-Time Tracking;
    6 Correlated System and Measurement Noise Processes: The Affine Model; Optimal Estimate Operators; Effect on Optimal Estimation with Additional Data; Derivation of Kalman Filtering Equations; Real-Time Applications; Linear Deterministic/Stochastic Systems;
    7 Coloured Noise: Outline of Procedure; Error Estimates; Kalman Filtering Process; White System Noise; Real-Time Applications;
    8 Limiting Kalman Filter: Preliminary Results; Geometric Convergence; Real-Time Applications. Sequential and Square-Root Algorithms: Sequential Algorithm; Square-Root Algorithm;
    An Algorithm for Real-Time Applications.
    9 Extended Kalman Filter and System Identification: Extended Kalman Filter; Satellite Orbit Estimation; Adaptive System Identification; An Example of Constant Parameter Identification; Modified Extended Kalman Filter; Time-Varying Parameter Identification;
    10 Kalman Filtering for Interval Systems: Interval Mathematics; Interval Kalman Filtering; Weighted-Average Interval Kalman Filtering;
    11,12 Other filtering techniques and applications
    Specific Course Requirements
    None
    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
    Activity Type Group/Individual Weight Due
    Assignment Summative
    (Hurdle Requirement)
    Individual 50% TBA
    Exam Summative
    (Hurdle Requirement)
    Individual 50% End of Semester
    Assessment Related Requirements
    The examination and assignment are prescribed summative assessment exercises in which students must obtain at least 40% in the exam in order to pass the course.

    A hurdle requirement is defined by the University's Assessment for Coursework Programs policy as "...an assessment task mandating a minimum level of performance as a condition of passing the course.
    If a student fails to meet a hurdle requirement (normally no less than 40%),and is assigned a total mark for the course in the range of 45-49, then the student is entitled to an offer of additional assessment of some type. The type of assessment is to be decided by the School Assessment Review Committee when determining final results. The student’s final total mark will be entered at no more than 49% and the offer of an additional assessment will be specified eg. US01. Once the additional assessment has been completed, this mark will be included in the calculation of the total mark for the course and the better of the two results will apply. Note however that the maximum final result for a course in which a student has sat an additional assessment will be a “50 Pass”.

    If a student is unable to meet a hurdle requirement related to an assessment piece (may be throughout semester or at semester’s end) due to medical or compassionate circumstances beyond their control, then the student is entitled to an offer of replacement assessment of some type. An interim result of RP will be entered for the student, and the student will be notified of the offer of a replacement assessment. Once the replacement assessment has been completed, the result of that assessment will be included in the calculation of the total mark for the course.
    Assessment Detail

    Assignments: The work will be assessed to the technical content of its presentation, the effectiveness of the assignment report. The marking scheme is published with the instructions for the assignment.

    Exam: the examination at the end of the semester will be of two hours duration and will be closed book.

    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 date and must be accompanied by a signed cover sheet. Copies of blank cover sheets are available from the School office in Ingkarni Wardli, room 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/undergraduate/
    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.

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  • Policies & Guidelines
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