Evaluation of data associations for active and passive sensors

Multiple-sensor data fusion is becoming increasingly important among the defence community as technology evolves. The use of multiple-sensor information reduces the ambiguity and presents the operator an enhance tactical picture of the surveillance volume. The crucial step in the fusion process is the data association of the information received from the sensors. The reason is that if the associations are made incorrectly then the fused data could potentially give rise to estimates that might be worse than those of a single sensor.; In this thesis, we explore the problem of associating Electronic Support Measure (ESM) observations and tracks with one or more possible radar tracks. We examine the performance of different track-to-track and observation-to-track data association algorithms. The effect of coordinate systems on the performance of the association techniques is also explored in this thesis. De-centralized data association approaches using likelihood functions are evaluated and compared to that of the centralized techniques. The performance of these techniques is based on the Probability of False Association (Pfa) and Probability of Correct (Pc) association.; The simulation results reveal that while the de-centralized and centralized data association techniques yield similar probability of correct association, their probability of false association depends mainly on the scenario as well as the technique under investigation.