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Decentralised data fusion

This is a three-day intensive course in the theory and application of decentralised data fusion (DDF) methods. The course is:

  • aimed at professional engineers and research scientists wishing to acquire a practical knowledge of DDF principles
  • covers essential methods in decentralised tracking and identification, communication in decentralised sensor networks, sensor and network management, sensor node and sensor network design
  • emphasises the application of DDF methods on specific demonstrator systems, focusing on issues of interface and network design
  • includes a series of lectures, together with practical computer-based laboratories aimed at demonstrating implementations, and
  • based on industry courses previously given by staff of the ACFR.

Objectives

  • The course aims to provide practical knowledge and skills for engineers and scientists wishing to employ and develop DDF systems.
  • The course is mathematically advanced but emphasises practical implementations in ground and airborne sensor networks.
  • A key feature of the course is the use of practical laboratory sessions, based on Matlab, in which DDF methods are implemented and evaluated by students.

Outcomes

  • To provide students with the theoretical and practical skills necessary to design, implement and evaluate DDF algorithms.
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Syllabus

  1. Distributed Data Fusion Methods: Probabilistic methods in decentralised data fusion, information theory models, multi-sensor estimation. Laboratories: Data fusion with Bayes theorem, Multi-sensor tracking.
  2. Estimation and Communication: DDF systems, decentralised multi-target tracking, communication in decentralised systems. Laboratories: Decentralised multi-target tracking, Operation of sensor networks.
  3. Advanced Methods and Systems: Communication management, sensor management, decentralised navigation and control, decentralised picture compilation, large-scale networks. Laboratories: Decentralised sensor management, Large-scale networks.

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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