A UAV Detection Algorithm Based on an Artificial Neural Network

Zhang, Hao; Cao, Conghui; Xu, Lingwei; Gulliver, T. Aaron

doi:10.1109/access.2018.2831911

Cited by 74 publications

(38 citation statements)

References 25 publications

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“…ANNs have two major applications for wireless VR. First, ANNs can be used to predict the users' movement as well as their future interactions with the VR [95] • Resource management ⇒ DNN-based RL algorithm • UAV detection [93] • Limited time for data collection • Channel modeling for air-to-ground ⇒ SNN-based algorithm • Deployment and caching [92], [96], and [97] • Errors in training data • Handover for UE UAVs ⇒ RNN-based algorithm • Design multi-hop aerial network ⇒ CNN-based algorithm • UE UAV trajectory prediction ⇒ SNN-based algorithm VR • Resource allocation [103], [104] • Errors in collected data • VR users' movement ⇒ RNNs prediction algorithm • Head movement prediction [105] • Limited computational resources • Content correlation ⇒ CNN-based algorithm • Gaze prediction [106] • Limited time for training ANNs • VR video coding and decoding ⇒ CNN-based algorithm • Content caching and transmission [107] • Correction of inaccurate VR images ⇒ CNN-based algorithm • Viewing video prediction ⇒ SNN-based algorithm • Joint wireless and VR user environment prediction ⇒ RNNs prediction algorithm • Manage computational resources and video formats ⇒ DNN-based RL algorithm…”

Section: Wireless Virtual Realitymentioning

confidence: 99%

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Chen

Challita

Saad

et al. 2019

IEEE Commun. Surv. Tutorials

842

418

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In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based machine learning algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important for wireless network design. Such examples include echo state networks, liquid state machine, and long short term memory. And then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks and edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this article constitutes the first holistic tutorial on the development of ANN-based machine learning techniques tailored to the needs of future wireless networks. arXiv:1710.02913v2 [cs.IT] 30 Jun 2019• Input gate (i t ): controls whether the input is passed on to the memory cell or ignored. • Output gate (o t ): controls whether the current activation

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Section: Wireless Virtual Realitymentioning

confidence: 99%

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Chen

Challita

Saad

et al. 2019

IEEE Commun. Surv. Tutorials

842

418

View full text Add to dashboard Cite

show abstract

“…The passive detection of low-altitude signal sources is evaluated in this section using the advanced method (AM) [2], and the constant false alarm rate (CFAR) [28], higher-order cumulant (HOC) [29], and proposed methods. For the CFAR algorithm, the two-dimensional (2-D) energy window slides over the entire time-frequency matrix to detect the signal source.…”

Section: Performance Resultsmentioning

confidence: 99%

“…The detection and management of low-altitude signal sources have recently attracted significant research interest [1]. An unmanned aerial vehicle (UAV) is a typical low-altitude signal source which communicates with a controller using radio frequency (RF) signals [2]. According to a Consumer Electronics Association (CEA) survey, global sales of UAVs reached 69 million in 2015 and may exceed 1 billion by 2020.…”

Section: Introductionmentioning

confidence: 99%

“…Many techniques have been proposed to detect aerial targets including laser scanners, acoustic detectors, infrared thermal cameras, and visual observations. Acoustic detection is only effective over short distances of 300 m or less [2]. Distinguishing different low-altitude signal sources beyond 1.5 km is difficult using infrared thermal cameras and visual observations [8].…”

Section: Introductionmentioning

confidence: 99%

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Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm

et al. 2018

Self Cite

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The passive detection of low-altitude signal sources is studied using an improved cross-correlation method in the time–frequency domain. A matching template is designed for signal cross-correlation, and a cross-correlation threshold is used to determine whether a signal source is present or not. An improved cross-correlation method is also proposed to estimate the direction of arrival and communication frequency of a signal source. Furthermore, the distance and signal-to-noise ratio are estimated using an energy detector. Outdoor data from a bridge in the Jimo District, Qingdao, and indoor data from a research laboratory are used for performance evaluation. The results obtained show that the proposed method can provide better passive detection of low-altitude signal sources compared to several well-known algorithms in the literature. In addition, this method is more suitable for long-distance detection.

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“…However, this MAC address based detection method is vulnerable to interference, and wireless protocols must be known beforehand. Zhang et al [25] proposed a detection algorithm based on an Artificial Neural Network (ANN) where the recognition rate is greater than 82% within a distance of 3 km. Fu et al [26] presented an SDR-based, portable universal software radio peripheral (USRP) system for detection in two scenarios.…”

Section: Introductionmentioning

confidence: 99%

Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

Zhao

Chen

et al. 2018

Applied Sciences

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Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented.

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A UAV Detection Algorithm Based on an Artificial Neural Network

Cited by 74 publications

References 25 publications

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm

Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

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