An Assessment of Track Fusion Algorithms Public
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Radar is a cornerstone of modern intelligence, surveillance and reconnaissance. While radar can determine the location of a target to within a region of space, fundamental uncertainties exist that limit the accuracy of individual radars. A method that is used to reduce these uncertainties is known as data fusion and involves processing the measurements from multiple radars together. One of the main challenges of using data fusion in the field is the difficulty of being able to associate individual detections that correspond to the same target into tracks in real time. Different data fusion algorithms exist to reduce the computation time but the trade-off is lower track accuracy. The goal of the MQP was to quantify the trade-offs for different data fusion algorithms under several scenarios.
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