Authors:
Yaqin Wang
1
;
Facundo Esquivel Fagiani
2
;
Kar Ee Ho
1
and
Eric T. Matson
1
Affiliations:
1
Computer and Information Technology, Purdue University, West Lafayette, IN, U.S.A.
;
2
Renard Analytics, San Miguel de Tucumán, Argentina
Keyword(s):
Audio Classification, UAV Classification, Machine Learning, Drone Security, Payload Detection, Acoustic Classification, Neural Network, Feature Extraction.
Abstract:
The technology evolution of Unmanned Aerial Vehicles (UAVs) or drones, has made these devices suitable for a wide new range of applications, but it has also raised safety concerns as drones can be used for carrying explosives or weapons with malicious intentions. In this paper, Machine Learning (ML) algorithms are used to identify drones carrying payloads based on the sound signals they emit. We evaluate and propose a feature-based classification. Five individual features, and one combinations of features are used to train four different standard machine learning models: SupportVector Machine (SVM), Gaussian Naive Bayes (GNB), K-Nearest Neighbor (KNN) and a Neural Network (NN) model. The training and testing dataset is composed of sound samples of loaded drones and unloaded drones collected by the team. The results show that the combination of features outperforms the individual ones, with much higher accuracy scores.