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Estimation and the kalman filter (KC-1)

This is a three day intensive course providing an introduction to advanced sensor-based estimation and the application of the Kalman filter. The course is:

  • aimed at professional engineers and research scientists wishing to acquire a practical knowledge of advanced estimation methods and with particular interest in sensor-based estimation, tracking, sensor data fusion, and real-time state estimation methods
  • includes a series of lectures, practical laboratories and tutorials, and
  • based on a large number of courses given by staff of the ACFR over the past 10 years.

Lecturer

Objectives

  • The course aims to provide practical knowledge and skills for engineers and scientists wishing to employ and develop advanced estimation methods.
  • The focus of the course is on the (linear) Kalman filter and the (non-linear) extended Kalman filter and their use in sensor-based estimation problems.
  • A key feature of the course is the use of practical laboratory sessions, based on Matlab, in which estimation methods are implemented and evaluated.

Outcomes

  • To provide both the theoretical and practical skills necessary to design and implement advanced state estimation algorithms.
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Prerequisites

  • This course is intended for practising control or systems engineers, first year graduate students or equivalent.
  • Students are expected to have prior experience with probability theory, statistical methods, and state-space modelling techniques.

Syllabus

  1. Probabilistic models for sensors and systems, probabilistic estimation methods (ML, MAP, MMSE), linear estimation. Laboratory: Probabilistic models for sensors and systems.
  2. The linear Kalman filter, understanding and implementing the linear Kalman filter; Laboratory: Target tracking using the linear Kalman filter.
  3. The extended Kalman Filter, understanding and implementing the extended Kalman filter. Laboratory: Land vehicle navigation using the extended Kalman filter.

Enrolment/Timing

  • This course is run periodically at the ACFR facilities at The University of Sydney, Sydney, Australia.
  • The course can be run at a company/institute site, providing appropriate (PC) facilities are made available for laboratory work.
  • Maximum enrolment of 15 students.
  • For more information, please contact us - see Enquiries.

Course materials

  • Course materials consist of comprehensive course notes, slides used in course presentation, source code for laboratories, tutorials and tutorial solutions. Course material is provided in both printed hard-copy and in soft-copy (CD-ROM) form.

Name and password required for the following information (Instructions: Create a directory for the course. Place laboratory software modules in different directories. Instructions for laboratories is in zipped files.):

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