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Motivated by security needs in unmanned aerial system (UAS) operations, analgorithm for identifying airspace intruders (e.g., birds vs. drones) isdeveloped. The algorithm is structured to use sensed intruder velocity datafrom Internet-of-Things platforms together with limited knowledge of physicalmodels. The identification problem is posed as a statistical hypothesis testingor detection problem, wherein inertial feedback-controlled objects subject tostochastic actuation must be distinguished by speed data. The maximum aposteriori probability detector is obtained, and then is simplified to anexplicit computation based on two points in the sample autocorrelation of thedata. The simplified form allows computationally-friendly implementation of thealgorithm, and simplified learning from archived data. Also, the totalprobability of error of the detector is computed and characterized. Simulationsbased on synthesized data are presented to illustrate and supplement the formalanalyses.

Distinguishing Aerial Intruders from Trajectory Data: A Model-Based Hypothesis-Testing Approach