ELEC ENG 7002 - Kalman Filtering & Applications
North Terrace Campus - Semester 2 - 2015
General Course Information
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 Coordinator: Professor Peng ShiEmail: email@example.com
Phone: 8313 6424
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesAt 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
Required ResourcesThere 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 LearningExtensive 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.
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 RequirementsNone
Small Group Discovery ExperienceNot applicable
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Activity Type Group/Individual Weight Due Assignment Summative
Individual 50% TBA Exam Summative
Individual 50% End of Semester
Assessment Related RequirementsThe 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.
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Exam: the examination at the end of the semester will be of two hours duration and will be closed book.
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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
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