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ELEC ENG 7085 - Multisensor Data fusion

North Terrace Campus - Semester 1 - 2017

Elementary applications and techniques for data fusion in military and civilian systems; Inference; Classification; Multisensor classification; Tracking; and Multisensor registration.

  • General Course Information
    Course Details
    Course Code ELEC ENG 7085
    Course Multisensor Data fusion
    Coordinating Unit School of Electrical & Electronic Engineering
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Online - 40 hours total for semester and 104 hours assignments and exam
    Available for Study Abroad and Exchange Y
    Prerequisites ELEC ENG 7071 Detection Estimation and Classification
    Incompatible SIP 7005 Multisensor & Data Fusion
    Assumed Knowledge Linear algebra (matrices), differential equations (linear systems) and complex analysis (Laplace transforms), probability theory and MATLAB
    Assessment 4 assignments 20% each, take home exam online 20%
    Course Staff

    Course Coordinator: Samuel Davey

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    University Graduate Attributes

    No information currently available.

  • Learning Resources
    Required Resources
    A set of course notes, and other supporting materials will also be available for downloading from the MyUni course web site.
    Recommended Resources
    The following text books are not required but are a good source of additional information:
    "Tracking and Data Fusion: A Handbook of Algorithms" , Y Bar-Shalom, P K Willett, and X Tian, 2011
    "Design and Analysis of Modern Tracking Systems", S S Blackman and R Popoli, 1999
    "Multitarget-multisensor tracking: principles and techniques", Y Bar-Shalom and X R Li, 1995
    Online Learning
    Extensive use will be made of the MyUni web site for this course,  
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on self directed learning. Resources will be provided to the students but there will be no formal lectures. The lecturer will be available to assist and informal tutorials may be scheduled as required.
    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 30
    Tutorials 10
    Homeworks 36
    Exam 4
    TOTALS 10 70
    Learning Activities Summary
    Activity Week Topic
    Lecture 1 Introduction, multi sensor fusion issues
    2 Dynamic models
    3 The Kalman Filter
    4 The Extended Kalman Filter
    5 The Particle Filter
    6 Manoeuvring Targets
    7 Data Association, Global Nearest Neighbours
    8 Probabilistic Data Association
    9 Joint Probabilistic Data Association
    10 Track-to-track fusion
    11 Covariance Intersection, Decentralised Fusion
    12 Sensor Registration, Track Management
    13 Further Topics
    Tutorial 3 Multi-sensor fusion, dynamic models, Kalman Filter
    5 Extended Kalman Filter, Particle Filter
    7 Manoeuvring Targets, Data Association, Global Nearest Neighbours
    11 Probabilistic Data Association, JPDA, track-to-track fusion
    13 CI, DDF, Registration, management, review
  • 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 outcomes addressed
    Homeworks Formative 80% Weeks 4 6 8 12 All
    Exam Summative - Hurdle 20% End of semester All
    Assessment Related Requirements
    The examination is a hurdle requirement. It is necessary to achieve at least 40% in the exam. If this is not achieved, the total course mark will be limited to a maximum of 49.

    A hurdle requirement is defined by the University's 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
    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:
     
    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 .

  • 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

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