RF Fingerprint Measurement For Detecting Multiple Amateur Drones Based on STFT and Feature Reduction

Xu, Chengtao; Chen, Bowen; Liu, Yongxin; He, Fengyu; Song, Houbing

doi:10.1109/icns50378.2020.9223013

Cited by 29 publications

(11 citation statements)

References 12 publications

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“…The simplified architecture of the proposed sensor that provides direct data input for drone detection methods proposed in [20], [22]- [24] is demonstrated in Fig. 3.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Rather than send the separate time-domain I and Q components to the PC, the proposed sensor delivers one 12-bit formatted frequency-domain sample. A bitstream throughput is reduced by half with this approach, and produced data can be applied in detection methods based on machine learning or spectrogram features proposed by [22]- [24]. The data rate could be reduced even further, to about 10 MB/s, by employing the adapted binarization module.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Drone Detection Sensor With Continuous 2.4 GHz ISM Band Coverage Based on Cost-Effective SDR Platform

Flak

2021

IEEE Access

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The development of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has introduced revolutionary changes in many areas over the past few years. However, aside from opening new possibilities, the usage of drones in an irresponsible and dangerous manner leads to many hazardous incidents. This paper presents a drone detection sensor with a continuous 2.400 GHz-2.483 GHz operational frequency range for detection methods based on passive radio frequency imaging techniques. The implementation based on Software Defined Radio (SDR) and Field Programmable Logic Array (FPGA) hardware that overcomes the 40 MHz real-time bandwidth limit of other popular SDRs is presented utilizing low-cost off-the-shelf components. Furthermore, hardware realization of the signal processing chain for specific detection algorithms is proposed to minimize the throughput between SDR and the companion computer and offload software computations. The device validation is made in a laboratory and real-life scenario and presented in relation to the sensor used in other works. The measurements indicate some detection sensitivity reduction compared to the reference receiver, consistent with hardware specifications. The final analysis demonstrates the proposed device's relevance as a sensor for obtaining machine learning datasets and as a part of a final anti-drone system.

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“…The simplified architecture of the proposed sensor that provides direct data input for drone detection methods proposed in [20], [22]- [24] is demonstrated in Fig. 3.…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Drone Detection Sensor With Continuous 2.4 GHz ISM Band Coverage Based on Cost-Effective SDR Platform

Flak

2021

IEEE Access

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show abstract

“…Different from the abovementioned methods, methods based on RF signals can be applied in the real world more easily, being less constrained by UAV shapes and the uncertainties in the acquisition environment. Meanwhile, the UAV RF signals can be captured at a long distance and contain abundant information about the UAVs’ flight modes [ 22 , 34 ], which cannot be easily achieved by other methods.…”

Section: Related Workmentioning

confidence: 99%

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

Yang

Luo

Miao

et al. 2021

Entropy

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With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

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“…The third method involves recognizing the UAV by detecting the characteristics of its VS. Ezuma [13] uses bandwidth analysis for recognizing the UAV, and Luan Haiyan [17] proposed a method to calculate the fourth-order cumulant to distinguish the UAV VS from other single-carrier modulation signals. The method in [18] extracts the sliding kurtosis, skewness, and slope features of the UAV VS spectrum, which are then input to a machine learning scheme to recognize the UAV VS. Xu et al [19] proposed an RF detection method that employs a short time fourier transform (STFT) and feature reduction strategy combined with machine learning, which can detect the number of UAVs.…”

Section: Introductionmentioning

confidence: 99%

Recognition of UAV Video Signal Using RF Fingerprints in the Presence of WiFi Interference

et al. 2021

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This paper investigates the problem of unmanned aerial vehicle (UAV) recognition in the presence of WiFi interference using passive radio frequency (RF) detection. The proposed method relies on machine learning based RF recognition and considers the scenario in the bandwidth of the video signal (VS) and WiFi are identical. Our machine learning strategy involves extracting 31 features from the WiFi signal and the UAV VS, which are then input to the classifier. Among the 31 features, 30 are statistical in the time and frequency domain, while the remaining one involves the effective subcarrier feature. We evaluate four different machine learning (ML) classifier variants and demonstrate through simulation and experiments that the proposed method can accurately recognize UAV VS in the presence of WiFi interference. We also improve the feature-vector compactness and reduce the 31-feature vector to a 6-feature vector composed of the most significant features and demonstrate that the recognition performance of random forest method (RandF) classifier is not compromised. The RandF obtains the best result, which has a recognition rate of 100% in the indoor experiment. While in the 2 km outdoor experiment, the recognition rate of the four ML classifiers is larger than 95.52%, which is better than other UAV detection methods such as radar, acoustic and video.INDEX TERMS UAV video signal, recognition, WiFi interference, machine learning.Ming Zuo Author received Bachelor's degree in Communication Engineering from Jiangxi Normal University, Nanchang, China, in 2014, and he Master's degree in Electronic and Communication Engineering from Nanchang University, Nanchang, China, in 2016. He is currently pursuing the PHD degree with the School of Electronic and Information Engineering, Beihang University. His research interests focus on radar signal processing, electronic countermeasure, passive positioning, anti-unmanned aerial vehicles.

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RF Fingerprint Measurement For Detecting Multiple Amateur Drones Based on STFT and Feature Reduction

Cited by 29 publications

References 12 publications

Drone Detection Sensor With Continuous 2.4 GHz ISM Band Coverage Based on Cost-Effective SDR Platform

Drone Detection Sensor With Continuous 2.4 GHz ISM Band Coverage Based on Cost-Effective SDR Platform

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

Recognition of UAV Video Signal Using RF Fingerprints in the Presence of WiFi Interference

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