UAV Detection and Identification Based on WiFi Signal and RF Fingerprint

Nie, Wei; Han, Zhi-Chao; Zhou, Mu; Xie, Liangbo; Jiang, Qing

doi:10.1109/jsen.2021.3068444

Cited by 69 publications

(37 citation statements)

References 41 publications

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“…At the same time, our method has significantly higher accuracy under the same SNR. Compared to [9], our method is able to identify a wider variety of drones with comparable accuracy.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The accuracy of the K-Nearest Neighbor (KNN) can reach 98.13% when the SNR is 25dB. In [9], fractal dimension (FD), axially integrated bispectra (AIB) and square integrated bispectra (SIB) of the signals are calculated respectively. The average accuracy is 100%, 97.23% and 96.11%, corresponding to the above three characteristics.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the idea of feature extraction and classification recognition is also adopted by Olusiji, O. [6] and Nie, W. [9]. The comparison of recognition effects of different methods is shown in Table 1.…”

Section: Experiments and Analysismentioning

confidence: 99%

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UAV signal recognition method based on re-classification and separation of image transmission signal and flight control signal

Wang

Lv²,

Yi³

et al. 2022

International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022)

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The rapid development of the drone industry brings security risks to low-altitude airspace in important places. In order to effectively defense unmanned aerial vehicles (UAV), it is of great significance to develop a system which can identify drone models by radio signal. To identify similar UAVs, a UAV signal recognition method based on re-classification and separation of image transmission signal (ITS) and flight control signal (FCS) is proposed. The OFDM time domain parameters of ITS are extracted for model pre-classification. Then, after extracting the time-frequency features of ITS and FCS respectively, the UAV can be identified with high precision by SVM. The experimental results show that the average accuracy of the four DJI UAVs can reach more than 95% when the signal-to-noise ratio (SNR) is 0dB.

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“…At the same time, our method has significantly higher accuracy under the same SNR. Compared to [9], our method is able to identify a wider variety of drones with comparable accuracy.…”

Section: Experiments and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

UAV signal recognition method based on re-classification and separation of image transmission signal and flight control signal

Wang

Lv²,

Yi³

et al. 2022

International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022)

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“…ML techniques including SVM, Random Forest (RF) [62], Naive Bayes (NB), Ensemble Learning (EL), and KNN are then used to detect the drone. The authors in [61] also proposed in [63] to extract other features from CSI including Fractal Dimension (FD), Axially Integrated Bispectra (AIB), and Square Integrated Bispectra (SIB) and then applied PCA [64] and NCA [65] methods to reduce the feature dimensionality and trained ML classifiers (SVM, KNN) to perform the detection task. The aforementioned works used traditional ML algorithms, which typically does not provide sufficient accuracy and robust performance in practical large deployments due to the non-linearity in the RF signals.…”

Section: B Rf-based Detectionmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

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The market size of civilian drones is tremendously increasing and is expected to reach 1.66 million by the end of 2023. The increase in number of civilian drones poses several privacy and security threats. To safeguard critical assets and infrastructure and to protect privacy of people from the illegitimate uses of commercial drones, a drone detection system is inevitable. In particular, there is a need for a drone detection system that is efficient, accurate, robust, costeffective and scalable. Recognizing the importance of the problem, several drone detection approaches have been proposed over time. However, none of these provides sufficient performance due to the inherited limitations of the underlying detection technology. More specifically, there are trade-offs among various performance metrics e.g., accuracy, detection range, and robustness against environmental conditions etc. This motivates an in-depth study and critical analysis of the existing approaches, highlighting their potential benefits and limitations. In this paper, we provide a rigorous overview of the existing drone detection techniques and a critical review of the state-of-the-art. Based on the review, we provide key insights on the future drone detection systems. We believe these insights will provide researchers and practicing engineers a holistic view to understand the broader context of the drone detection problem.

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“…Another UAV detection and identification approach is based on Wi-Fi signal and radio fingerprint, as presented in [ 25 ]. Firstly, the system detects the presence of a UAV, and features from RF signal are extracted using Machine Learning and Principal Component Analysis-derived techniques to extract RF fingerprints.…”

Section: Review Of the State-of-the-art Technologymentioning

confidence: 99%

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Besada

Campaña

Carramiñana

et al. 2021

Sensors

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Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors’ performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.

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UAV Detection and Identification Based on WiFi Signal and RF Fingerprint

Cited by 69 publications

References 41 publications

UAV signal recognition method based on re-classification and separation of image transmission signal and flight control signal

UAV signal recognition method based on re-classification and separation of image transmission signal and flight control signal

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

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