Relevant bibliographies by topics / Drone detection tracking and neutralization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Drone detection tracking and neutralization'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Drone detection tracking and neutralization"

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GONZALEZ JORGE, HIGINIO, ENRIQUE ALDAO PENSADO, GABRIEL FONTELA CARRERA, FERNANDO VEIGA LOPEZ, EDUARDO BALVIS OUTEIRIÑO, and EDUARDO RIOS OTERO. "ANTI-DRONE TECHNOLOGY." DYNA 99, no. 6 (2024): 599–607. http://dx.doi.org/10.52152/d11227.

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This work provides an overview of the growing use of drones in both civilian and military domains, highlighting the associated risks to public safety and privacy. It discusses the development of counter-drone systems to mitigate these risks, focusing on detection, tracking, classification, and neutralization technologies. Various sensors such as passive radar, acoustic sensors, electro-optical and infrared sensors, radiofrequency analyzers, active radar, and LiDAR are detailed, along with algorithms for data fusion. Mitigation strategies include soft kill methods like spoofing and jamming, as
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Marian, Buric, and Cubber Geert De. "Counter Remotely Piloted Aircraft Systems." MTA Review 27, no. 1 (2017): 9–18. https://doi.org/10.5281/zenodo.1115502.

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An effective Counter Remotely Aircraft System is a major objective of many researchers and industries entities. Their activity is strongly impelled by the operational requirements of the Law Enforcement Authorities and naturally follows both the course of the latest terrorist events and technological developments. The designing process of an effective Counter Remotely Aircraft System needs to benefit from a systemic approach, starting from the legal aspects, and ending with the technical ones. From a technical point of view, the system has to work according to the five “kill chain”
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Kim, Joosung, and Inwhee Joe. "Deep Learning-Based Drone Defense System for Autonomous Detection and Mitigation of Balloon-Borne Threats." Electronics 14, no. 8 (2025): 1553. https://doi.org/10.3390/electronics14081553.

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In recent years, balloon-borne threats carrying hazardous or explosive materials have emerged as a novel form of asymmetric terrorism, posing serious challenges to public safety. In response to this evolving threat, this study presents an AI-driven autonomous drone defense system capable of real-time detection, tracking, and neutralization of airborne hazards. The proposed framework integrates state-of-the-art deep learning models, including YOLO (You Only Look Once) for fast and accurate object detection, and convolutional neural networks (CNNs) for X-ray image analysis, enabling precise iden
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Yuan, Yubin, Yiquan Wu, Langyue Zhao, Yaxuan Pang, and Yuqi Liu. "Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern." Drones 8, no. 8 (2024): 349. http://dx.doi.org/10.3390/drones8080349.

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Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance featu
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Truong, Xuan Tung. "DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING." Journal of Military Science and Technology, no. 73 (June 15, 2021): 10–19. http://dx.doi.org/10.54939/1859-1043.j.mst.73.2021.10-19.

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The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained mod
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Aouladhadj, Driss, Ettien Kpre, Virginie Deniau, Aymane Kharchouf, Christophe Gransart, and Christophe Gaquière. "Drone Detection and Tracking Using RF Identification Signals." Sensors 23, no. 17 (2023): 7650. http://dx.doi.org/10.3390/s23177650.

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The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented
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Hong, Tao, Qiye Yang, Peng Wang, et al. "Multitarget Real-Time Tracking Algorithm for UAV IoT." Wireless Communications and Mobile Computing 2021 (August 24, 2021): 1–15. http://dx.doi.org/10.1155/2021/9999596.

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Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In
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Rudys, Saulius, Andrius Laučys, Paulius Ragulis, et al. "Hostile UAV Detection and Neutralization Using a UAV System." Drones 6, no. 9 (2022): 250. http://dx.doi.org/10.3390/drones6090250.

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The technologies of Unmanned Aerial Vehicles (UAVs) have seen extremely rapid development in recent years. UAV technologies are being developed much faster than the means of their legislation. There have been many means of UAV detection and neutralization proposed in recent research; nonetheless, all of them have serious disadvantages. The essential problems in the detection of UAVs is the small size of UAVs, weak radio wave reflection, weak radio signal, and sound emitting. The main problem of conventional UAV countermeasures is the short detection and neutralization range. The authors propos
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Panduri, Bharathi, P. K. Abhilash, T. N. P. Madhuri, Venkata Naga Tejaswi Bethapudi, and Muntather Almusawi. "Sustainable Drone Detection using Deep Learning Paradigms." E3S Web of Conferences 529 (2024): 04005. http://dx.doi.org/10.1051/e3sconf/202452904005.

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Drones are becoming more and more common, which has advantages but also concerns. They can be used to support illegal operations like drug trafficking and endanger places that are important to security. While advances in sensor technology haven't produced reliable answers in the literature, current drone detection and neutralization methods frequently require previous detection and categorization. Using radio frequency (RF) signals and a frequency signature-based deep learning model, this work promotes an environmentally friendly and multidisciplinary method of drone identification and categor
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Zbigniew Koruba and Izabela Krzysztofik. "Dynamics and Control of the Gyroscopic Head used for the Laser Illumination of a Ground Target from the Quadcopter Deck." Communications - Scientific letters of the University of Zilina 23, no. 2 (2021): B158—B164. http://dx.doi.org/10.26552/com.c.2021.2.b158-b164.

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In the paper authors investigate dynamics of a controlled quadcopter in terms of the possibility of its use for detection, observation, tracking and laser illuminating of both stationary and moving ground targets in the conditions of impact of random and kinematic excitations. The drone is equipped with a scanning and tracking Gyroscopic Head (GH) coupled with a laser target indicator. The drone is affected by random disturbances in the form of wind gusts or explosions of missiles. Kinematic excitations, such as drone maneuvers and vibrations from engines, act on the GH. This paper focuses mai
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Dissertations / Theses on the topic "Drone detection tracking and neutralization"

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Aouladhadj, Driss. "Méthodes de détection et de reconnaissance de modèles de drone par surveillance et analyse de l'activité radio fréquence." Electronic Thesis or Diss., Université Gustave Eiffel, 2023. http://www.theses.fr/2023UEFL2067.

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Le développement des drones et leur accès à des coûts de plus en plus bas représentent une menace, notamment pour les sites les plus sensibles et les grands événements publics. La surveillance du ciel est devenue essentielle pour s'assurer qu'un drone ne pénètre pas dans une zone critique ou ne menace pas des foules en transportant, par exemple, des matériaux explosifs. Les techniques de surveillance couramment utilisées, basées sur la détection visuelle, thermique ou radar, présentent des limitations en milieu urbain à cause d'obstacles comme les bâtiments, la petite taille des drones et les
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Lukáč, Jakub. "Sledování osob v záznamu z dronu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417275.

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Práca rieši možnosť zaznamenávať pozíciu osôb v zázname z kamery drona a určovať ich polohu. Absolútna pozícia sledovanej osoby je odvodená vzhľadom k pozícii kamery, teda vzhľadom k umiestneniu drona vybaveného príslušnými senzormi. Zistené dáta sú po ich spracovaní vykreslené ako príslušné cesty. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácie jednotlivých osôb v čase, určenie vzdialenosti objektu od kamery, spracovanie potrebných senzorových dát. Následne využiť preskúmané metódy a navrhnúť riešenie, ktoré bude v reálnom čase
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Šťastný, Filip. "Mapování trajektorií pohybu chodců v záznamu pořízeným dronem." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417276.

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This master's thesis deals with pedestrian detection using neural networks in a video record captured by drone. Pedestrians are tracked, and their GPS coordinates are calculated using digital elevation models and mapped based on their identity and an information provided by the drone.
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Lukáč, Jakub. "Sledování osob ve videu z dronu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445483.

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Práca rieši možnosť zaznamenávať pozíciu osôb v zázname z kamery drona a určovať ich polohu. Absolútna pozícia sledovanej osoby je odvodená vzhľadom k pozícii kamery, teda vzhľadom k umiestneniu drona vybaveného príslušnými senzormi. Zistené dáta sú po ich spracovaní vykreslené ako príslušné cesty v grafe. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácia jednotlivých osôb v čase, určenie vzdialenosti objektu od kamery, spracovanie potrebných senzorových dát. Následne využiť preskúmané metódy a navrhnúť riešenie, ktoré bude v reál
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KOLÁŘ, Michal. "Visual tracking systém pro UAV." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-375321.

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This master thesis deals with the analysis of the current possibilities for object tracking in the image, based on which is designed a procedure for creating a system capable of tracking an object of interest. Part of this work is designing virtual reality for the needs of implementation of the tracking system, which is finally deployed and tested on a real prototype of unmanned vehicle.
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(8800964), Maria Nieves Brunet Avalos. "Stereo vision-based system for detection, track and capture of intruder flying drones." Thesis, 2020.

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<div>In this thesis, the design and implementation of an autonomous system that will equip a multi-rotor unmanned aerial vehicle (UAV) for visual detection and tracking of other UAVs is presented. The results from detection and tracking are used for real-time motion planning.</div><div><br></div><div>The goal is to effectively detect unwanted UAVs, track them and finally capture them with a net. Having a net that traps the UAVs and enables dragging intruders to another location is of great importance, since these could be carrying dangerous loads.</div><div><br></div><div>The project consists
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Book chapters on the topic "Drone detection tracking and neutralization"

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Xing, Daitao, Halil Utku Unlu, Nikolaos Evangeliou, and Anthony Tzes. "Drone Surveillance Using Detection, Tracking and Classification Techniques." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13324-4_38.

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Cifuentes-García, Cristian, Daniel González-Medina, and Ismael García-Varea. "People Detection and Tracking Using an On-Board Drone Camera." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36150-1_55.

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Zhang, Yunxuan. "TAD: Tracking-Aided Detection Siamese Network for Visual Drone Surveillance." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_309.

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Zhu, Pengfei, Longyin Wen, Dawei Du, et al. "VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11021-5_29.

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Pranoto, Musthafa Dimas Bagaskoro, Muhammad Ikhsan Sani, and Marlindia Ike Sari. "Aerial Object Tracking System on Micro Quadrotor Drone for Crowd Detection in Small-Scale Area." In Advances in Social Science, Education and Humanities Research. Atlantis Press SARL, 2023. http://dx.doi.org/10.2991/978-2-38476-132-6_84.

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Bisio, Igor, Chiara Garibotto, Halar Haleem, Fabio Lavagetto, and Andrea Sciarrone. "Drone surveillance system—RF/WiFi-based drone detection, localization, and tracking: a survey." In Aviation Cybersecurity: Foundations, principles, and applications. Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/sbra545e_ch3.

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Zhang, Xiangzhou, and Zhongke Shi. "A New Method for Lightweight Extracting Traffic Parameters from Drone Videos and Experimental Verification." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240381.

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In order to study the accurate extraction of vehicle traffic parameter information from drone aerial videos to improve urban traffic intelligent management and auxiliary modeling of car-following behavior. A new method for lightweight extraction of vehicle following behavior parameters from drone videos is proposed in this article. The improved ShuffleNet network and GSConv module were introduced into the Yolov7-tiny neural network model as the target detection stage. HOG features and IOU motion metrics are introduced into the DeepSort multi-object tracking algorithm as the tracking matching s
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E. Álvarez-Cisneros, Izyalith, Blanca E. Carvajal-Gámez, David Araujo-Díaz, Miguel A. Castillo-Martínez, and L. Méndez-Segundo. "Smart-Road: Road Damage Estimation Using a Mobile Device." In Visual Object Tracking [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.100289.

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Mexico is located on five tectonic plates, which when moving, generate telluric movements. These movements, depending on their intensity, affect the telecommunications infrastructure. Earthquakes tend to cause landslides, subsidence, damage to structures in houses, buildings, and roads. In the case of road damage, it is reflected in cracks in the pavement, which are classified according to their size, shape, and depth. The methods that are currently implemented to inspect roads mainly use human perception and are limited to a superficial inspection of the terrain, causing this process ineffect
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More, Sharmila Sharad, Aruna Rana, Bakhtawer Shameem, and Bhavna Narain. "Revolutionizing Military Operations: The Role of Deep Learning with YOLO v7 in the Evolution of Drones." In Advancements in Intelligent Systems. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-975670-3-2-4.

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In this research paper we explores the transformative role of drones in military operations, highlighting the historical evolution and strategic impact of these unmanned systems. Tracing their development from early investigation tools to sophisticated battle instruments, the study highlights significant milestones in drone technology. We are giving special attention to the integration of deep learning techniques and the YOLOv7 object detection framework, to examining their contributions for enhancing drone capabilities. The paper investigates into the technical advancements brought by deep le
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Yao, Zhao, and Hejia Li. "Development of Multi-Objective Spatial Position Testing Technology." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240750.

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In recent years, single motion target detection, positioning, and tracking technology has been widely applied in fields such as national defense, military, traffic monitoring vehicle positioning, and drone target tracking. In response to the difficulty of detecting and locating abnormal spatial distribution of multiple targets in complex background environment conditions, in order to meet the demand for real-time and accurate localization of multiple targets, a dual station camera is used to capture multiple target image sequences. Based on target detection and multi-target tracking technology
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Conference papers on the topic "Drone detection tracking and neutralization"

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AlDosari, Khloud, AIbtisam Osman, Omar Elharrouss, Somaya Al-Maadeed, and Mohamed Zied Chaari. "Drone-type-Set: Drone types detection benchmark for drone detection and tracking." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620104.

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Bin Zainudin, Ahmad Zaki, Hezerul Bin Abdul Karim, and Nouar AlDahoul. "Drone Detection and Tracking in Distorted Surveillance Video." In 2024 IEEE 8th International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2024. http://dx.doi.org/10.1109/icsipa62061.2024.10686164.

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Zainudin, Ahmad Zaki Bin, Hezerul Bin Abdul Karim, and Nouar AlDahoul. "Drone Detection and Tracking in Distorted Surveillance Video." In 2024 Multimedia University Engineering Conference (MECON). IEEE, 2024. https://doi.org/10.1109/mecon62796.2024.10776099.

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Ancy Micheal, A., and Sneha Sivaramakrishnan. "Human Detection and Tracking for Drone based Marine Surveillance." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723860.

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Geng, Zhe, Bilendo Delvis Da Vida, Chongqi Xu, Shiyu Zhang, Xiang Yu, and Daiyin Zhu. "Drone-based bistatic SAR-infrared cross-modality vehicle detection and classification." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050540.

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Arie Wicaksana, Hendra Bagus, Ronny Mardiyanto, and Astria Nur Irfansyah. "Drone Position Tracking System based on Object Detection and ArUco Marker for Autonomous Navigation Applications." In 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2024. http://dx.doi.org/10.1109/isitia63062.2024.10668046.

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Wang, Peng, Yongcai Wang, and Deying Li. "DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610941.

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R, Nandakumar, Sridevi S, Gopi J, Naveena E, Pradeepika M, and Varsha T. "Autonomous Drone for High Accuracy Gender & Age Detection, Location Tracking and Speech Recognition using Machine Learning." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985047.

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Srihari, Pathipati, Vandana G. S, Uday Kumar, Ashwin Nandagiri, Bethi Pardhasaradhi, and Linga Reddy Cenkeramaddi. "FMCW Radar-Based Detection and Tracking of Drones Using DBSCAN Clustering and Extended Kalman Filter for Anti-Drone Defense Systems." In 2024 IEEE 21st India Council International Conference (INDICON). IEEE, 2024. https://doi.org/10.1109/indicon63790.2024.10958522.

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Rahulkumar, Parmar, Parmar Yashwantkumar, Dhruvi Shrimali, Keval Bhavsar, and Umang Parmar. "Drone detection and tracking using electro optics." In PROCEEDINGS ON SMART AND SUSTAINABLE DEVELOPMENTS IN ENGINEERING AND TECHNOLOGY: (PICET 2023). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0208395.

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