Thesis Abstracts 2001
Research and Graduate Studies Electrical and Computer Engineering
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Evaluation of Data Association for Active and Passive Sensors
By: Capt Denis Gendron, M. Eng. (Elec. Eng.)
Supervisors: Dr. K. Benameur and Dr. M. Farooq
Abstract
In this thesis, the problem of Target Motion Analysis (TMA) based on measurements from radar and ESM sensors is considered. We emphasise on the association techniques used to fuse information from both sensors. Multiple-sensor data fusion is an increasingly rising concern among the defence community as technology evolves. Data fusion or information fusion is defined as "locate and identify an unknown number of unknown objects of many different types on the basis of different kinds of evidence. This evidence is collected on an ongoing basis by many possibly re-allocable sensors having varying capabilities."
The basis of the data fusion process is the use of redundancy and diversity of information contained in multiple, overlapping observations over a surveillance domain to achieve a combined view that is better than any individual sensor observations. Many different sensors may provide these observations. So, the concern is to try to make better use of the existing systems (different sensors) by fusing the information, to provide the operators with a better tactical picture. Data fusion spans from military applications like battlefield surveillance, threat assessment and tactical situation assessment, as well as non-military applications like robotics, remote sensing, and automated manufacturing.
The problem considered is how to associate Electronic Support Measure (ESM) observations and tracks with one or more of m possible radar tracks. Different association algorithms have been evaluated for different scenarios and different dynamics of the targets. The experimental results show that while the de-centralized and centralized data association techniques have similar probability of correct association, their probability of false association was very different depending on the scenario and the technique utilised. In general, the centralized probability of false association is much better.
