Abstract
Unmanned Aerial Vehicles UAVs have revolutionized a wide range of activities and businesses, creating new opportunities for commercial and military applications. However, they also pose potential risks for terrorist and criminal activities. Therefore, researchers have been examining potential drone threats and considering how to address security and privacy concerns related to this technology. This research topic has gained significant attention in recent years due to the rapid proliferation of commercial and recreational drones, as well as the associated risks to airspace safety and security. Various detection technologies have been developed, including radar, optical, and acoustic sensing systems. However, each detector has its own limitations, such as reduced effectiveness in low-light conditions, fog, and noise. To address these limitations, we have developed a drone detection technique that utilizes multiple detectors, including visual, acoustic, and magnetic field sensors applying artificial intelligence, to compensate for their shortcomings. In this approach, each detector makes an independent decision, which may be either consistent or conflicting with the other detectors. In our study, we employed the Bayesian Inference technique to optimize decision-making in cases where there was conflict among the decisions made by the multi-sensors. We used indicators such as the Ephemeris indicator (EI) and the Acoustic ambiance indicator (AI) to generate settings to determine the degree of conflict. The drone detection process was fully automated, and the conflict decision was optimized using this approach. Our results indicated that the automatic drone detection with Bayesian inference had the best performance for drone identification in terms of accuracy, specificity, and sensitivity, as well as the highest accuracy in preventing unwanted drone interventions.
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The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Belmote ML, Morales R, Fernandez-Caballero A (2019) Computer vision in autonomous unmanned aerial vehivles-a systematic mapping study: A review. Academic Open Access Publishing (MDPI), 9
Laith A, Diabat A, Putra S, Amir G (2021) Applications, deployments, and integration of internet of drones (iod): a review. IEEE Sensors J, 21
Adel G, Anuar B, Ruzairi B, ASM S, Shukor MA (2022) Gismalla: Involvement of surveillance drones in smart cities: Asystematic review. IEEE Access Publishing
Lykou G, Moustakas D, Gritzalis D (2020) Review: Defending airports from uas: A survey oncyber-attacks and counter-drone sensing. MDPI Academic Open Access Publishing
Hassanalian Darvishpoor S, Roshanian J, Raissi A (2007) A review of configurations, flight mechanisms and applications of unmanned aerial systems. Prog Aerosp Sci, Science Direct, 121. https://doi.org/10.1016/j.paerosci.100694
Seidaliyeva U, Akhmetov D, Llipbayeva L, Matson E (2020) Real-time and accurate drone detection in a video with static background. Sensors, 20
Singhal G, Bansod B, Mathew L (2021) Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges. IEEE Sensors J, 21
Morrow A (2014) Review: Couple accuses neighbor of stalking with drone. USA Today
Gritzalis DA, Grammati P, Roman Castro R (2021) Sensors cybersecurity. MDPI Sensors, 21. https://doi.org/10.3390/s21051762
Hicks P (2020) Watch the skies. J Int Secur, 2:22–24
Seongjoon P, Hyeontae TK, Sangmin L, Hyeontae J, Hwangnam K (2021) Survey on anti-drone systems:components,designs,and challenges. J IEEE Access, 9
Nguyen P, Truong H, Ravindranathan M, Nguyen A, Han R, Vu T (2018) Cost-effective and passive rf-based drone presence detection and characterization. Paper presented at ACM international conference on mobile systems, applications, and services with permission 21:30–34
Mototolea D, Stolk C (2018) Detection and localization of small drones using commercial of the-shelf fpga based software defined radio systems. In: Proceedings of the international conference on communications(COMM), Bucharest, Romania, pp 465–470
Bernardini A, Mangiatordi F, Pallotti E, Capodiferro L (2017) Drone detection by acoustic signature identification. Electron Imaging, 10:60–64
Park S, Shin S, Kim Y, Matson ET, Lee K, Kolodzy PJ, Slater JC, Scherreik M, Sam M, Gallagher JC (2015) Combination of radar and audio sensors for identification of rotor-type unmanned aerial vehicles (uavs). In: Proceedings of the IEEE SENSORS, pp 1–4
Goldman S, Borel-Donohue G, Christoph C (2017) Detection of unmanned aerial vehicles using a visible camera system. Appl Opt, 3:214–221
Tammina S (2019) Transfer learning using vgg-16 with deep convolutional neural network for classifying images. Int J Sci Res Publ, 9(10):143–150
Zhang L, Yingming GZZ, Nianyu Z (2020) a lightweight distributed measurement method used to study illuminance estimation. Elsevier
Simon CL (2015) les archives sonores: de l’archivistique a la creation. In: Communication presented by canadienne association of bibliography,archives, and documentation center
Ayodele AF, Johnson TS (2021) The effects of aircraft noise on psychosocial health. J Trans Health, Elsevier, 22. https://doi.org/10.1016/j.jth.2021.101230
Ripka P, Janosek M (2010) Advances in magnetic field sensors. IEEE Sensors J, 10:(06)
Michael J, Caruso D, Carl TH, Smith, Bratland RS (1998) A new perspective on magnetic field sensing. Sensors-Petrbourough, pp 34–47
Sentence S, Waite J, Yeomans UL, MacLeod E (2017) Teaching with physical computing devices: the bbc micro-bit initiative. ACM Digital Library, pp 87–96
Colombo M, Series P (2012) Bayes in the brain -on bayesian modelling in neuroscience. Br J Philos Sci
Baldo JV, Kacinuk N, Carl SN, Paulraj , Moncreif A, Piai V, Curran B, Herron T, Dronkers FN (2018) Voxel-based lesion analysis of brain regions underlying reading and writing. Elsevier, pp 51–59
Adesnik H, Naka A (2018) Cracking the function of layers in the sensory cortex. Elsevier, 5
Donoghue J (2001) Neural representations of intended movement in motor cortex. Pergamon
Friston K, Schwartenbeck P, FitzGerald T, Moutoussis M, Behrens T, Dolan R (2022) The anatomy of choice: dopamine and decision-making. Phil Trans R Soc. https://doi.org/10.1098/rstb.2013.0481
Pinghe N, Jun L, Hong H, Qiang H, Xiuli D (2021) probalistic model updating via bayesian inference and adaptive gaussian process modeling. Comput Methods Appl Mech Eng, Science Direct, 383. https://doi.org/10.1016/j.cma.2021.113915
Thrane E, Talbot C (2019) An introduction to bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models. Centre for Astrophysics, Cambridge University Press. https://doi.org/10.1017/pas.2020.xxx
Taha B, Shoufan A (2019) Machine learning-based drone detection and classification: State-of-the-art in research. IEEE Access, 7
Asif Khan M, Menouar H, Eldeeb A, Abu-Dayya A, Salim FD (2022) On the detection of unauthorized drones-techniques and future perspectives: A review. IEEE Sensors J, 22
Nalamati M, Kapoor A, Saqib M, Sharma N, Blumenstein M (2019) Drone detection in long-range surveillance videos. In:16th IEEE International conference on advanced video and signal based surveillance (AVSS)
Shovon MHI, Gopalan R, Campbell B (2023) A comparative analysis of deep learning algorithms for optical drone detection. In: 15th International conference on machine vision ICMV, 12701
Utebayeva D, Ilipbayeva L, Matson ET (2022) Practical study of recurent neural networks for efficient real-time drone sound detection: A review. Drones MDP I:7
Lacava G, Mercaldo F, Martenelli F, Santone A, Pizzi M (2022) Drone audio recognition based on machine learning techniques. Proc Comput Sci, 207
Lipovskýa P, Novotňáka J, Blažeka J (2022) Possible utilization of low frequency magnetic fields in short range multirotor uav detection system. In: 11th International conference on air transport - INAIR, 65
Ding S, Guo X, Peng T, Huang X, Hong X (2023) Drone detection and tracking system based on fused acoustical and optical approaches. Adv Intell Syst
Svanström F, Alonso-Fernandez F, Englund C (2021) A dataset for multi-sensor drone detection. Data in Brief
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Saadaoui, F.Z., Cheggaga, N. & Djabri, N.E.H. Multi-sensory system for UAVs detection using Bayesian inference. Appl Intell 53, 29818–29844 (2023). https://doi.org/10.1007/s10489-023-05027-z
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DOI: https://doi.org/10.1007/s10489-023-05027-z