Artificial Intelligence
Members
Activities
The research unit Artificial Intelligence aims at developing solutions for assisting decision makers in crisis management and military command & control situations, by providing them with an easy and intelligent access to the information they need.
The information can be extracted from the raw data produced by different types of sensors using a wide range of signal and image procession techniques. It can however also be obtained from higher level external information sources, for instance intelligence reports or information exchanges with other information systems.
The research unit is therefore interested in image processing techniques for extracting information from image data, as well as in data fusion approaches for combining different elements of information into a single operational picture.
The unit is also interested in developing better ways of making the data and information accessible to the human decision maker, whatever the device he or she is using, as well as in the exchange of information between different command and control systems in order to realize a common operational picture for all the decision makers involved in an operation.
Research projects
Military multi-Agent System For APT Detection (MASFAD)
The projects consists in the development of an Advanced Persistent Threat (APT) detection demonstrator, in a consortium together with TNO and FKIE financed by the European Defense Agency. RMA is prime contractor.
SUM (Surveillance in an Urban environment using Mobile sensors)
The aim of this project is to develop a low-cost multi-sensor vehicle protection system using a data fusion engine in order to enhance situational awareness and aid command and control for a moving vehicle in an urban environment, as well as to protect critical static points such as road blocks or outposts, taking into account the operational needs defined by end-users. The system will be able to detect potential threats at large distances while driving at a normal cruising speed. Furthermore, the system will be able to recognize and characterize threats in more detail at smaller distances and at slower speeds. Based on the working conditions and the operator\'s instructions, the system will be able to switch between operating modes for the overall system and for the individual sensors (spot mode, scanning mode etc.)
Publications
- Wim Mees. Risk management in coalition networks. In Third International Symposium on Information Assurance and Security, pages 329-336. IEEE, August 2007.
- W. Mees. Detection of defects in a fuzzy knowledge base. In Proceedings of the IEEE International Fuzzy Systems Conference., pages 204-209 vol.1. IEEE, 1999.