Abstract
Advances in Unmanned Aircraft System (UAS) technologies have made them smaller and more affordable, causing a proliferation in UAS activity. This increase has led to a rise in UAS-related incidents, exposing the need for stricter UAS regulation. Detection, Localization, and Classification (DLC) of UAS are important techniques for UAS regulation. However, DLC can be challenging, since UAS span a variety of shapes, sizes, speeds, propulsion systems, and operational altitudes; they can be hard to detect with radar; and they can operate in radio silence. Research supporting DLC of UAS has led to the development of novel, passive acoustic signal processing algorithms for real-time classification. In particular, data fusion algorithms combining kinetic and acoustic data into features for classification are being developed. Kinetic data are collected using acoustic signal source tracking algorithms, and spectral content is extracted from frequency-domain analysis. Using free-body modeling and Doppler compensation, classification probabilities can then be calculated for a list of known UAS. Initial classification algorithms have been developed and tested in MATLAB using UAS data collected in operational environments. The test results will also be presented.