You are here: Home / ACFR / Education / Industry training

Multisensor data fusion (KC-3)

This is a three day intensive course in the theory and application of multisensor data fusion methods.

The course is:

  • aimed at professional engineers and research scientists wishing to acquire a practical knowledge of data fusion methods
  • covers essential methods in multi-sensor estimation, identification, distributed and decentralised data fusion methods
  • emphasises applications in multi-sensor tracking, distributed sensor systems and multi-sensor navigation
  • includes a series of lectures, together with practical computer-based laboratories/tutorials aimed at demonstrating implementations, and
  • based on a number of graduate and industry courses given by staff of the ACFR over the past 10 years.

Objectives

  • The course aims to provide practical knowledge and skills for engineers and scientists wishing to employ and develop multi-sensor data fusion systems.
  • The focus of the course is on multiple-sensor estimation methods including the multi-sensor Kalman filter, the information filter and multi-sensor discrete and continuous Bayesian estimators.
  • The course emphasises practical implementations in both civilian and military applications.
  • A key feature of the course is the use of practical laboratory sessions, based on Matlab, in which data fusion methods are implemented and evaluated.

Outcomes

  • To provide both the theoretical and practical skills necessary to design and implement data fusion algorithms.
Back to top

Prerequisites

  • This course is intended for practising control or systems engineers, advanced graduate students or equivalent.
  • Students are expected to have prior experience with state estimation methods (equivalent to KC-1).

Syllabus

  1. Probabilistic models, discrete and continuous Bayesian and information fusion methods. Laboratory: Data fusion with Bayes theorem.
  2. The multisensor Kalman filter, non-linear Multisensor methods, multisensor multi-target tracking. Laboratory: Multisensor multi-target tracking.
  3. Distributed and decentralised data fusion methods, multi-person decision theory. Laboratory: Distributed and decentralised data fusion.

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.):


Back to top