{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T12:58:09Z","timestamp":1753880289134,"version":"3.41.2"},"reference-count":19,"publisher":"ASME International","issue":"3","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Unmanned aerial vehicles (UAVs) are fast becoming a low-cost, affordable tool for various security and surveillance tasks. It has led to the use of UAVs (drones) for unlawful activities such as spying or infringing on restricted or private air spaces. This rogue use of drone technology makes it challenging for security agencies to maintain the safety of many critical infrastructures. Additionally, because of the drones\u2019 varied low-cost design and agility, it has become challenging to identify and track them using conventional radar systems. This paper proposes a deep reinforcement learning-based approach for identifying and tracking an intruder drone using a chaser drone. Our proposed solution employs computer vision techniques interleaved with a deep reinforcement learning control for tracking the intruder drone within the chaser\u2019s field of view. The complete end-to-end system has been implemented using robot operating system and Gazebo, with an Ardupilot-based flight controller for flight stabilization and maneuverability. The proposed approach has been evaluated on multiple dynamic scenarios of intruders\u2019 trajectories and compared with a proportional-integral-derivative-based controller. The results show that the deep reinforcement learning policy achieves a tracking accuracy of 85%. The intruder localization module is able to localize drones in 98.5% of the frames. Furthermore, the learned policy can track the intruder even when there is a change in the speed or orientation of the intruder drone.<\/jats:p>","DOI":"10.1115\/1.4067601","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T15:36:02Z","timestamp":1736436962000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":0,"title":["Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone"],"prefix":"10.1115","volume":"25","author":[{"given":"Shivam","family":"Kainth","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Ropar Department of Computer Science Engineering, , , \u00a0 ,","place":["Rupnagar, Punjab, India, 140001"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shashi Shekhar","family":"Jha","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Ropar Department of Computer Science Engineering, , , \u00a0 ,","place":["Rupnagar, Punjab, India, 140001"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"issue":"1","key":"2025020618135792100_CIT0001","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/MWC.2018.1800023","article-title":"Cellular-Connected UAV: Potential, Challenges, and Promising Technologies","volume":"26","author":"Zeng","year":"2018","journal-title":"IEEE Wireless Commun."},{"key":"2025020618135792100_CIT0002","first-page":"1","article-title":"Decentralized Critical Area Coverage Using Multi-UAV System With Guided Explorations During Floods","author":"Garg","year":"2023","journal-title":"CASE"},{"issue":"1","key":"2025020618135792100_CIT0003","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1109\/TCCN.2021.3099114","article-title":"Combined RF-Based Drone Detection and Classification","volume":"8","author":"Basak","year":"2021","journal-title":"IEEE Trans. Cognit. Commun. Networking"},{"issue":"3","key":"2025020618135792100_CIT0004","doi-asserted-by":"publisher","first-page":"4552","DOI":"10.1109\/LRA.2021.3068952","article-title":"Decentralized Multi-agent Pursuit Using Deep Reinforcement Learning","volume":"6","author":"De Souza","year":"2021","journal-title":"IEEE Rob. Autom. Lett."},{"key":"2025020618135792100_CIT0005","first-page":"1","article-title":"Chasing the Intruder: A Reinforcement Learning Approach for Tracking Unidentified Drones","author":"Kainth","year":"2023"},{"issue":"19","key":"2025020618135792100_CIT0006","doi-asserted-by":"publisher","first-page":"4332","DOI":"10.3390\/s19194332","article-title":"Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning","volume":"19","author":"Opromolla","year":"2019","journal-title":"Sensors"},{"issue":"1","key":"2025020618135792100_CIT0007","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41074-019-0059","article-title":"Deep Learning-Based Strategies for the Detection and Tracking of Drones Using Several Cameras","volume":"11","author":"Unlu","year":"2019","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"issue":"15","key":"2025020618135792100_CIT0008","doi-asserted-by":"publisher","first-page":"23723","DOI":"10.1007\/s11042-020-10231-x","article-title":"Autonomous Long-Range Drone Detection System for Critical Infrastructure Safety","volume":"80","author":"Zhang","year":"2021","journal-title":"Multimedia Tools Appl."},{"year":"2021","author":"Barisic","key":"2025020618135792100_CIT0009"},{"key":"2025020618135792100_CIT0010","first-page":"4128","article-title":"Flying Objects Detection From a Single Moving Camera","author":"Rozantsev","year":"2015"},{"issue":"5","key":"2025020618135792100_CIT0011","doi-asserted-by":"publisher","first-page":"768","DOI":"10.3390\/electronics9050768","article-title":"Improved Kalman Filter Variants for UAV Tracking With Radar Motion Models","volume":"9","author":"Wei","year":"2020","journal-title":"Electronics"},{"key":"2025020618135792100_CIT0012","first-page":"1","article-title":"Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation","author":"Pham","year":"2018"},{"issue":"3","key":"2025020618135792100_CIT0013","doi-asserted-by":"publisher","first-page":"58","DOI":"10.3390\/drones3030058","article-title":"Drones Chasing Drones: Reinforcement Learning and Deep Search Area Proposal","volume":"3","author":"Akhloufi","year":"2019","journal-title":"Drones"},{"key":"2025020618135792100_CIT0014","first-page":"1","article-title":"Drone, Aircraft and Bird Identification in Video Images Using Object Tracking and Residual Neural Networks","author":"Fernandes","year":"2019"},{"key":"2025020618135792100_CIT0015","first-page":"8386","article-title":"Object Detection of UAV for Anti-UAV Based on Improved Yolo V3","author":"Hu","year":"2019"},{"issue":"4","key":"2025020618135792100_CIT0016","doi-asserted-by":"publisher","first-page":"522","DOI":"10.3390\/agriculture14040522","article-title":"Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms","volume":"14","author":"Gao","year":"2024","journal-title":"Agriculture"},{"issue":"1","key":"2025020618135792100_CIT0017","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/TNET.2023.3297876","article-title":"A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones","volume":"32","author":"Zeng","year":"2024","journal-title":"IEEE\/ACM Trans. Networking"},{"issue":"2","key":"2025020618135792100_CIT0018","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3390\/s22020464","article-title":"Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs","volume":"22","author":"Nepal","year":"2022","journal-title":"Sensors"},{"key":"2025020618135792100_CIT0019","first-page":"387","article-title":"Deterministic Policy Gradient Algorithms","author":"Silver","year":"2014","journal-title":"Proceedings of Machine Learning Research"}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/25\/3\/031008\/7422297\/jcise-24-1071.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/25\/3\/031008\/7422297\/jcise-24-1071.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T18:14:04Z","timestamp":1738865644000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/25\/3\/031008\/1211155\/Deep-Reinforcement-Learning-Based-Localization-and"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,6]]},"references-count":19,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4067601","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"type":"print","value":"1530-9827"},{"type":"electronic","value":"1944-7078"}],"subject":[],"published":{"date-parts":[[2025,2,6]]},"article-number":"031008"}}