Air Traffic Control (ATC) radars are expected today to provide improved performance in terms of maximum range and spatial coverage, while the huge amount of small unwanted flying objects like birds and insects shall not lead to an increased false plot1 and / or false track rate. To accomplish these opposite aspects of radar capabilities, the occurring plots of the ATC primary surveillance radar (PSR) have to be assessed with respect to coming from wanted or unwanted objects. In addition to the various assessments usually implemented inside the ATC radar signal and data processing for this purpose, a new signature based plot classification is introduced to handle this challenging task. The plot classification recognises true air targets and discriminates them against echoes from birds, “angels”, wind turbines and other unwanted plots. The classification results are used to filter out false plots and to improve the plot to track association. Results from measurement campaigns show the benefit of this approach in real operational scenarios.
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