Activity Modeling and Characterization for Airport Bird Situation Awareness Using Avian Radar Datasets

Abstract

Birds in airport airspaces are critical threats to aviation safety. Avian radar systems are effective for long-range bird monitoring and hazard warning, but their functionalities are confined to a short-term temporal scale. Spatial–temporal activity modeling and characterization for birds are not studied comprehensively from historical radar datasets. This paper proposes a radar data analysis framework to characterize bird activities as a long-term functionality complement. Spatial domain modeling initializes data mining by extracting reference spots for data filtering. Bird activities are quantified in the temporal domain. Activity degrees are utilized for periodicity extraction with the daily segment random permutation strategy. Categorical probabilities are calculated to interpret bird activity periodicity characters. Historical radar datasets collected from an avian radar system are adopted for validation. The extracted activity periodicity trends for diurnal birds present prominent consistency with artificial observation records. Migratory bird periodicity trends present a good match with ornithology understandings. A preliminary experiment is presented to indicate the possibility of predicting bird activity levels, especially for migratory birds.