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, fundamental uncertainties exist that limit the accuracy of individual radars. Data fusion can reduce these uncertainties by combining measurements from multiple radars. A challenge is associating detections from destinct targets into accurate tracks. The MQP's goal was to quantify the trade-offs of different data fusion algorithms. This extension to the MQP reviews the background needed to understand radar and introduces the fundamentals of radar tracking and mathematical estimators. It then presents a breakdown of the two data fusion methods analyzed in the initial project in regards to the communications and CPU cost and the impact on both of down-sampling.
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Permanent link to this page: https://digital.wpi.edu/show/dr26z090z