K-Means Clustering Approach to UAS Classification via Graphical Signal Representation of Radio Frequency Signals for Air Traffic Early Warning

Abstract

Small Unmanned Aerial Systems (UASs) provide significant benefits to economies across the world on a daily basis but increased usage brings a number of security challenges. For example, identifying small UASs operating with malicious intent in a restricted airspace. Supervised learning techniques applied to radio frequency (RF) signals have been considered for the classification of UAS type with high accuracy but due to labelled data assume the UAS signal is already known. Unsupervised learning algorithms such as K-means clustering provide a potential for identifying small UAS signals which have not been seen before. The use of transfer learning and CNN feature extraction (FE) with spectrogram graphical signal representations have been successfully used in a supervised manner for medical diagnosis and audio classification. This research is the first application of transfer learning and CNN FE as a pre-cursor to an unsupervised learning algorithm. This paper shows that clustering graphical representations of the signal and utilising CNN FE with transfer learning produces the highest v-measure score 0.814 but at a cost of 6s in time. Small UASs can travel at speeds of 45 mph so timely detection is essential in many use cases. A decrease in 0.2 v-measure score using PSD graphical image representations of the RF signal and PCA initialisation allows the clustering time to complete in under 0.3s even in environments with active interference in the same band. This timely result could provide effective early warning with the cueing of a secondary sensor or supervised algorithm with higher classification accuracy.