  Motivated by security needs in unmanned aerial system (UAS) operations, an
algorithm for identifying airspace intruders (e.g., birds vs. drones) is
developed. The algorithm is structured to use sensed intruder velocity data
from Internet-of-Things platforms together with limited knowledge of physical
models. The identification problem is posed as a statistical hypothesis testing
or detection problem, wherein inertial feedback-controlled objects subject to
stochastic actuation must be distinguished by speed data. The maximum a
posteriori probability detector is obtained, and then is simplified to an
explicit computation based on two points in the sample autocorrelation of the
data. The simplified form allows computationally-friendly implementation of the
algorithm, and simplified learning from archived data. Also, the total
probability of error of the detector is computed and characterized. Simulations
based on synthesized data are presented to illustrate and supplement the formal
analyses.
