Unmanned Aerial Vehicle Hub Detection Using Software-Defined Radio

Xuemei, Huang; Yan, Kun; Wu, Hsiao-Chun; Wu, Yiyan

doi:10.1109/bmsb47279.2019.8971851

Cited by 12 publications

(13 citation statements)

References 17 publications

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“…Then, it uses the triangulation technique to estimate the location of RF signal peak sources. The free-space path loss model and triangulation combination is reported in [50] to detect and localize a stationary drone controller. The proposed system contains two direction-finding systems for direction identification and localization for the drone and its controller.…”

Section: Drone Controller Detection and Localizationmentioning

confidence: 99%

Drone and Controller Detection and Localization: Trends and Challenges

et al. 2022

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Unmanned aerial vehicles (UAVs) have emerged as a rapidly growing technology seeing unprecedented adoption in various application sectors due to their viability and low cost. However, UAVs have also been used to perform illegal and malicious actions, which have recently increased. This creates a need for technologies capable of detecting, classifying, and deactivating malicious and unauthorized drones. This paper reviews the trends and challenges of the most recent UAV detection methods, i.e., radio frequency-based (RF), radar, acoustic, and electro-optical, and localization methods. Our research covers different kinds of drones with a major focus on multirotors. The paper also highlights the features and limitations of the UAV detection systems and briefly surveys the UAV remote controller detection methods.

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Section: Drone Controller Detection and Localizationmentioning

confidence: 99%

Drone and Controller Detection and Localization: Trends and Challenges

et al. 2022

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“…Moreover, in urban and suburban environments with rich multipath propagation without line-of-sight (LOS) paths, the TDoA-or AoA-based methods may not be able to provide accurate results. To address these issues, a received signal strength indicator (RSSI)-based method has been proposed for drone controller localization [8]. In [8], the RSSI of a drone controller signal was measured and transformed into the propagation distance based on a path loss model.…”

Section: Introductionmentioning

confidence: 99%

“…To address these issues, a received signal strength indicator (RSSI)-based method has been proposed for drone controller localization [8]. In [8], the RSSI of a drone controller signal was measured and transformed into the propagation distance based on a path loss model. Then, a trilateration localization technique was adopted to estimate the location of the drone controller.…”

Section: Introductionmentioning

confidence: 99%

“…Then, a trilateration localization technique was adopted to estimate the location of the drone controller. However, it was found that if the path loss exponent n is a piecewise function of distance, ambiguity will occur, and moreover, if the path loss is not a monotonic function of distance, the method presented in [8] cannot be used. The authors of [9] proposed locating an RF source using the RSSI and trilateration.…”

Section: Introductionmentioning

confidence: 99%

“…First, we proposed two RSSI-ratio-based localization methods that outperformed the distance mapping algorithm proposed in [8] when tested in a WLAN channel. Accurate drone controller localization is crucial to effectively counteract the whole drone intrusion system.…”

Section: Introductionmentioning

confidence: 99%

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Drone Controller Localization Based on RSSI Ratio

Wang

Zeng

Sun

et al. 2023

Sensors

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We proposed two methods for the localization of drone controllers based on received signal strength indicator (RSSI) ratios: the RSSI ratio fingerprint method and the model-based RSSI ratio algorithm. To evaluate the performance of our proposed algorithms, we conducted both simulations and field trials. The simulation results show that our two proposed RSSI-ratio-based localization methods outperformed the distance mapping algorithm proposed in literature when tested in a WLAN channel. Moreover, increasing the number of sensors improved the localization performance. Averaging a number of RSSI ratio samples also improved the performance in propagation channels that did not exhibit location-dependent fading effects. However, in channels with location-dependent fading effects, averaging a number of RSSI ratio samples did not significantly improve the localization performance. Additionally, reducing the grid size improved the performance in channels with small shadowing factor values, but this only resulted in marginal gains in channels with larger shadowing factors. Our field trial results align with the simulation results in a two-ray ground reflection (TRGR) channel. Our methods provide a robust and effective solution for the localization of drone controllers using RSSI ratios.

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Radio frequency based distributed system for noncooperative UAV classification and positioning

Xue

2024

Journal of Information and Intelligence

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Unmanned Aerial Vehicle Hub Detection Using Software-Defined Radio

Cited by 12 publications

References 17 publications

Drone and Controller Detection and Localization: Trends and Challenges

Drone and Controller Detection and Localization: Trends and Challenges

Drone Controller Localization Based on RSSI Ratio

Radio frequency based distributed system for noncooperative UAV classification and positioning

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