TY - JOUR
T1 - Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs
AU - Zhao, Caidan
AU - Chen, Caiyun
AU - Cai, Zhibiao
AU - Shi, Mingxian
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - Beyond their benign uses, the small Unmanned Aerial Vehicles (UAVs) are expected to take the major role in future smart cities that have attracted the attention of the public and authorities. Therefore, detecting, tracking and classifying the type of UAVs is important for surveillance and air traffic management applications. Existing UAVs detection works focus on radars, visual detection, and acoustic sensors. However, the work was done by applying Support Vector Machine (SVM), k-Nearest Neighbor (KNN) based methods to classify the UAVs need a large number of samples for feature extraction to train a model. In this paper, we propose a new small UAVs classification system using Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGANs) based on the wireless signals collected from the UAVs of various types. Before the classification, using the Universal Software Radio Peripheral (USRP), oscilloscope and antenna to collect the wireless signals, preprocessing and dimensionality reduction to represent information at a lower dimension space. The processed data from UAVs is input to the UAVs' discriminant model of the AC-WGANs for classification. The obtained results show the effectiveness of the proposed system, which can achieve a recognition accuracy of around 95% in the indoor environment and can also be suitable in the outdoor environment.
AB - Beyond their benign uses, the small Unmanned Aerial Vehicles (UAVs) are expected to take the major role in future smart cities that have attracted the attention of the public and authorities. Therefore, detecting, tracking and classifying the type of UAVs is important for surveillance and air traffic management applications. Existing UAVs detection works focus on radars, visual detection, and acoustic sensors. However, the work was done by applying Support Vector Machine (SVM), k-Nearest Neighbor (KNN) based methods to classify the UAVs need a large number of samples for feature extraction to train a model. In this paper, we propose a new small UAVs classification system using Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGANs) based on the wireless signals collected from the UAVs of various types. Before the classification, using the Universal Software Radio Peripheral (USRP), oscilloscope and antenna to collect the wireless signals, preprocessing and dimensionality reduction to represent information at a lower dimension space. The processed data from UAVs is input to the UAVs' discriminant model of the AC-WGANs for classification. The obtained results show the effectiveness of the proposed system, which can achieve a recognition accuracy of around 95% in the indoor environment and can also be suitable in the outdoor environment.
KW - AC-WGANs
KW - classify model
KW - improved PCA
KW - UAVs
UR - http://www.scopus.com/inward/record.url?scp=85063520735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063520735&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2018.8647973
DO - 10.1109/GLOCOM.2018.8647973
M3 - Conference article
AN - SCOPUS:85063520735
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
M1 - 8647973
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
Y2 - 9 December 2018 through 13 December 2018
ER -