journal article·2025-10-31Open access

Optical FBG Sensor-Based System for Low-Flying UAV Detection and Localization

Ints Murans, Roberts Kristofers Zveja, Dilan Ortiz, Deomits Andrejevs, Niks Krumins, Olesja Novikova, Mykola Khobzei, Vladyslav Tkach, Andrii Samila, Aleksejs Kopats, Pauls Eriks Sics, Aleksandrs Ipatovs, Janis Braunfelds, Sandis Migla, Toms Salgals, Vjaceslavs Bobrovs

Applied Sciences· Volume 15· Issue 21

Abstract

With the recent increase in the threat posed by unmanned aerial vehicles (UAVs) operating in environments where conventional detection systems such as radar, optical, or acoustic detection are impractical, attention is paid to methods for detecting low-flying UAVs with small radar cross-section (RCS). The most commonly used detection methods are radar detection, which is susceptible to electromagnetic (EM) interference, and optical detection, which is susceptible to weather conditions and line-of-sight. This research aims to demonstrate the possibility of using passive optical fiber Bragg grating (FBG) as a sensitive element array for low-flying UAV detection and localization. The principle is as follows: an optical signal that propagates through an optical fiber can be modulated due to the FBG reaction on the air pressure caused by a low-flying (even hovering) UAV. As a result, a small target—the DJI Avata drone can be detected and tracked via intensity surge determination. In this paper, the experimental setup of the proposed FBG-based UAV detection system, measurement results, as well as methods for analyzing UAV-caused downwash are presented. High-speed data reading and processing were achieved for low-flying drones with the possible presence of EM clutter. The proposed system has shown the ability to, on average, detect an overpassing UAV’s flight height around 85 percent and the location around 87 percent of the time. The key advantage of the proposed approach is the comparatively straightforward implementation and the ability to detect low-flying targets in the presence of EM clutter.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Fiber Optic Sensors · Advanced Photonic Communication Systems · UAV Applications and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Photonics, Electronics and Telecommunications, Riga Technical University, 12 Azenes Street, LV-1048 Riga, Latvia

Department of Radio Engineering and Information Security, Yuriy Fedkovych Chernivtsi National University, 58000 Chernivtsi, Ukraine

Fiber Optical Sensor Research Group (RTU FiberSens), Riga Technical University, Azenes Street 12, LV-1048 Riga, Latvia

Funding

EU Recovery and Resilience Facility

References (48)

Show source-provided references
1. Kiss, B., Ballagi, Á., and Kuczmann, M. (2024). Overview Study of the Applications of Unmanned Aerial Vehicles in the Transportation Sector. Eng. Proc., 79.10.3390/engproc2024079011 ↗
2. A Literature Survey of Unmanned Aerial Vehicle Usage for Civil Applications10.1590/jatm.v13.1233 ↗
3. On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review10.1109/JSEN.2022.3171293 ↗
4. Advances in UAV detection: Integrating multi-sensor systems and AI for enhanced accuracy and efficiency10.1016/j.ijcip.2025.100744 ↗
5. Mandal, S., Chen, L., Alaparthy, V., and Cummings, M.L. (2020, January 6–10). Acoustic Detection of Drones through Real-time Audio Attribute Prediction. Proceedings of the AIAA Scitech 2020 Forum, Orlando, FL, USA.10.2514/6.2020-0491 ↗
6. An automated accurate sound-based amateur drone detection method based on skinny pattern10.1016/j.dsp.2023.104012 ↗
7. Lim, J., Joo, J., and Kim, S.C. (2025). Performance Enhancement of Drone Acoustic Source Localization Through Distributed Microphone Arrays. Sensors, 25.10.3390/s25061928 ↗
8. Liu, L., Sun, B., Li, J., Ma, R., Li, G., and Zhang, L. (2025, January 18–21). Localization of UAVs Using Acoustic Signals Collected by Distributed Acoustic-Electric Sensors. Proceedings of the 2025 IEEE 15th International Conference on Signal Processing, Communications and Computing (ICSPCC), Hong Kong.10.1109/ICSPCC66825.2025.11194606 ↗
9. Tejera-Berengue, D., Zhu-Zhou, F., Utrilla-Manso, M., Gil-Pita, R., and Rosa-Zurera, M. (2024). Analysis of Distance and Environmental Impact on UAV Acoustic Detection. Electronics, 13.10.20944/preprints202401.0209.v1 ↗
10. Drone Detection and Localization Using Enhanced Fiber-Optic Acoustic Sensor and Distributed Acoustic Sensing Technology10.1109/JLT.2022.3208451 ↗
11. Low-Altitude UAV Surveillance System via Highly Sensitive Distributed Acoustic Sensing10.1109/JSEN.2024.3452072 ↗
12. Fang, J., Li, Y., Ji, P.N., and Wang, T. (2022, January 6–10). Remote Drone Detection and Localization with Fiber-Optic Microphones and Distributed Acoustic Sensing. Proceedings of the 2022 Optical Fiber Communications Conference and Exhibition (OFC), San Diego, CA, USA.10.1364/OFC.2022.W4D.3 ↗
13. 300-Meter Long-Range Optical Camera Communication on RGB-LED-Equipped Drone and Object-Detecting Camera10.1109/ACCESS.2022.3177140 ↗
14. Zitar, R.A., Al-Betar, M., Ryalat, M., and Kassaymeh, S. (2023). A review of UAV Visual Detection and Tracking Methods. arXiv.
15. Liu, Z., An, P., Yang, Y., Qiu, S., Liu, Q., and Xu, X. (2024). Vision-Based Drone Detection in Complex Environments: A Survey. Drones, 8.10.3390/drones8110643 ↗
16. Seidaliyeva, U., Ilipbayeva, L., Utebayeva, D., Smailov, N., Matson, E.T., Tashtay, Y., Turumbetov, M., and Sabibolda, A. (2025). LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques. Sensors, 25.10.3390/s25092757 ↗
17. Gong, J., Yan, J., and Li, D. (2025, January 21–23). Radar Challenges and Solutions for Drone Detection. Proceedings of the 2025 26th International Radar Symposium (IRS), Hamburg, Germany.10.23919/IRS64527.2025.11046158 ↗
18. Larrat, M., and Sales, C. (2025). Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions. Sensors, 25.10.3390/s25030721 ↗
19. Khawaja, W., Ezuma, M., Semkin, V., Erden, F., Ozdemir, O., and Guvenc, I. (2025). A Survey on Detection, Classification, and Tracking of UAVs Using Radar and Communications Systems. IEEE Commun. Surv. Tutor.10.36227/techrxiv.174181637.73215309/v1 ↗
20. Gong, J., Yan, J., Kong, D., and Li, D. (2023). Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology. arXiv.
21. Deng, M., Ma, Y., and Wang, C. (2025, January 28–30). Design of Airborne Target Detection System with Fusion of Multi-Sensors. Proceedings of the 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA), Xi’an, China.
22. He, T., Hou, J., and Chen, D. (2025). Multimodal UAV Target Detection Method Based on Acousto-Optical Hybridization. Drones, 9.10.3390/drones9090627 ↗
23. Mehta, V., Dadboud, F., Bolic, M., and Mantegh, I. (2023, January 18–20). A Deep Learning Approach for Drone Detection and Classification Using Radar and Camera Sensor Fusion. Proceedings of the 2023 IEEE Sensors Applications Symposium (SAS), Ottawa, ON, Canada.10.1109/SAS58821.2023.10254123 ↗
24. Mazzeo, P.L., Frontoni, E., Sclaroff, S., and Distante, C. (2022). An Image-Based Classification Module for Data Fusion Anti-drone System. Proceedings of the International Conference on Image Analysis and Processing—ICIAP 2022 Workshops, Springer International Publishing.10.1007/978-3-031-13321-3 ↗
25. Frid, A., Ben-Shimol, Y., Manor, E., and Greenberg, S. (2024). Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks. Sensors, 24.10.3390/s24082427 ↗
26. CNN-Based UAV Detection and Classification Using Sensor Fusion10.1109/ACCESS.2023.3293124 ↗
27. Alhussein, A.N.D., Qaid, M.R.T.M., Agliullin, T., Valeev, B., Morozov, O., and Sakhabutdinov, A. (2025). Fiber Bragg Grating Sensors: Design, Applications, and Comparison with Other Sensing Technologies. Sensors, 25.10.3390/s25072289 ↗
28. Tang, Z., Ma, H., Qu, Y., and Mao, X. (2025). UAV Detection with Passive Radar: Algorithms, Applications, and Challenges. Drones, 9.10.3390/drones9010076 ↗
29. Toward a Fiber Bragg Grating-Based Two-Dimensional Force Sensor for Robot-Assisted Cardiac Interventions10.1109/LSENS.2021.3133571 ↗
30. A Cascade Fiber Optic Sensors for Simultaneous Measurement of Strain and Temperature10.1109/LSENS.2019.2949048 ↗
31. High Sensitivity Fiber Bragg Grating Humidity Sensors Made With Through-the-Coating Femtosecond Laser Writing and Polyimide Coating Thickening10.1109/LSENS.2024.3398432 ↗
32. Fiber Bragg Grating Sensor for Temperature Monitoring During HIFU Ablation of Ex Vivo Breast Fibroadenoma10.1109/LSENS.2019.2932910 ↗
33. Chourasia, R.K., and Katti, A. (2024). Bragg Fibers: From Optical Properties to Applications, Springer Nature.10.1007/978-3-031-65164-9 ↗
34. Advances in Fiber Bragg Grating (FBG) Sensing: A Review of Conventional and New Approaches and Novel Sensing Materials in Harsh and Emerging Industrial Sensing10.1109/JSEN.2024.3434351 ↗
35. Designing of Fiber Bragg Gratings for Long-Distance Optical Fiber Sensing Networks
36. Lab on Fiber Technology for biological sensing applications10.1002/lpor.201600111 ↗
37. Advanced fiber in-coupling through nanoprinted axially symmetric structures10.1063/5.0127370 ↗
38. Braunfelds, J., Senkans, U., Skels, P., Janeliukstis, R., Porins, J., Spolitis, S., and Bobrovs, V. (2022). Road Pavement Structural Health Monitoring by Embedded Fiber-Bragg-Grating-Based Optical Sensors. Sensors, 22.10.3390/s22124581 ↗
39. Senkans, U., Silkans, N., Spolitis, S., and Braunfelds, J. (2025). Comprehensive Analysis of FBG and Distributed Rayleigh, Brillouin, and Raman Optical Sensor-Based Solutions for Road Infrastructure Monitoring Applications. Sensors, 25.10.3390/s25175283 ↗
40. Kozlov, V., Kharchevskii, A., Rebenshtok, E., Bobrovs, V., Salgals, T., and Ginzburg, P. (2024). Universal Software Only Radar with All Waveforms Simultaneously on a Single Platform. Remote Sens., 16.10.3390/rs16111999 ↗
41. Numerical simulation of airflow field from a six–rotor plant protection drone using lattice Boltzmann method10.1016/j.biosystemseng.2020.07.018 ↗
42. Numerical analysis and validation of spray distributions disturbed by quad-rotor drone wake at different flight speeds10.1016/j.compag.2019.105036 ↗
43. CFD simulation on airflow behavior of quadcopter fertilizing drone for pineapple plantation10.1063/5.0122567 ↗
44. Mean and turbulent flow characteristics of downwash air flow generated by a single rotor blade in agricultural drones10.1016/j.compag.2021.106471 ↗
45. Ghirardelli, M., Kral, S.T., Müller, N.C., Hann, R., Cheynet, E., and Reuder, J. (2023). Flow Structure around a Multicopter Drone: A Computational Fluid Dynamics Analysis for Sensor Placement Considerations. Drones, 7.10.3390/drones7070467 ↗
46. Parra, P.H.G., Angulo, M.V.D., and Gaona, G.E.E. (2018, January 1–3). CFD Analysis of two and four blades for multirotor Unmanned Aerial Vehicle. Proceedings of the 2018 IEEE 2nd Colombian Conference on Robotics and Automation (CCRA), Barranquilla, Colombia.10.1109/CCRA.2018.8588130 ↗
47. Lahitani, A.R., Permanasari, A.E., and Setiawan, N.A. (2016, January 26–27). Cosine similarity to determine similarity measure: Study case in online essay assessment. Proceedings of the 2016 4th International Conference on Cyber and IT Service Management, Bandung, Indonesia.10.1109/CITSM.2016.7577578 ↗
48. ELECTRE II Method Based on the Cosine Similarity to Evaluate the Performance of Financial Logistics Enterprises under Double Hierarchy Hesitant Fuzzy Linguistic Environment10.1007/s10700-022-09382-3 ↗