Transactions of Nanjing University of Aeronautics & Astronautics
Detection and Classification on Amateur Drones Based on Cepstrum of Radio Frequency Signal
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Abstract:
As a prospective component of the future air transportation system， unmanned aerial vehicles （UAVs） have attracted enormous interest in both academia and industry. However， small UAVs are barely supervised in the current situation. Crash accidents or illegal airspace invading caused by these small drones affect public security negatively. To solve this security problem， we use the back-propagation neural network （BPNN）， the support-vector machine （SVM）， and the k-nearest neighbors （KNN） method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency （RF） signal of the drone’s downlink. The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment. The detection and classification algorithm based on the cepstrum properties is conducted. Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%. The mainstream downlink protocols of amateur drones can be classified effectively as well.
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TN925
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