Proceedings ArticleDOI
Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs
Caidan Zhao,Caiyun Chen,Zhibiao Cai,Mingxian Shi,Xiaojiang Du,Mohsen Guizani +5 more
- pp 206-212
TLDR
A new small UAVs classification system using Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGANs), which can achieve a recognition accuracy of around 95% in the indoor environment and can also be suitable in the outdoor environment is proposed.Abstract:
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.read more
Citations
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Journal ArticleDOI
Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference
TL;DR: This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system and investigates the performance of the NCA and five different ML classifiers for 15 different types of UAV controllers.
Journal ArticleDOI
Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery
TL;DR: A new diagnostic framework based on the adversarial neural networks (GAN) and multi-sensor data fusion technique to generate new synthetic data for data compensation purpose and it is concluded that the proposed Pre-fusion GAN and Post-fusions GAN frameworks both have good performance on the imbalanced fault diagnosis of rotating machinery.
Journal ArticleDOI
RF Fingerprinting Unmanned Aerial Vehicles With Non-Standard Transmitter Waveforms
Nasim Soltani,Guillem Reus-Muns,Batool Salehi,Jennifer G. Dy,Stratis Ioannidis,Kaushik R. Chowdhury +5 more
TL;DR: A multi-classifier scheme with a two-step score-based aggregation method, using RF data augmentation to increase neural network robustness to hovering-induced variations, and extending the multi- classifier scheme for detecting a new UAV, not seen earlier during training are proposed.
Journal ArticleDOI
Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification over Encrypted Wi-Fi Traffic
TL;DR: A machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic, which jointly optimizes feature selection and prediction performance in a unified objective function and utilizes maximum likelihood estimation (MLE) method to estimate the packet inter-arrival time.
Journal ArticleDOI
Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification Over Encrypted Wi-Fi Traffic
TL;DR: In this paper, a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic is proposed, which extracts features derived only from packet size and inter-arrival time of encrypted traffic, and can efficiently detect UAVs and identify their operation modes.
References
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Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TL;DR: This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
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Improved Training of Wasserstein GANs
TL;DR: This work proposes an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input, which performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning.
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Least Squares Generative Adversarial Networks
TL;DR: This paper proposes the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator, and shows that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence.
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
TL;DR: InfoGAN as mentioned in this paper is a generative adversarial network that maximizes the mutual information between a small subset of the latent variables and the observation, which can be interpreted as a variation of the Wake-Sleep algorithm.