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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM39896

AUTOMATIC DRONE DETECTION SYSTEM

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM39896

Volume:

8 |

12 |

Dec 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM39896

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VP

Vishnuvardhini P

UP

Udhayakumar P

PkS

Praveen kumar S

PR

Pradeep R

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Abstract

The Automatic Drone Detection System (ADDS) is designed to address the growing concern of unauthorized drone operations in restricted or sensitive airspaces. With the rapid proliferation of unmanned aerial vehicles (UAVs), there is a need for an effective solution to detect, track, and mitigate potential threats posed by drones. This system utilizes a combination of advanced technologies such as radar, machine learning algorithms, and computer vision to identify and classify drones in real-time. The system employs passive and active sensors, including radar and cameras, to monitor a specified airspace for UAV activity. By analyzing the captured data, the system can detect drones’ flight patterns, predict their trajectory, and distinguish them from other airborne objects. The use of machine learning models enhances the system’s accuracy in drone detection, reducing false positives and improving overall reliability. The ADDS aims to provide a comprehensive, automated solution for security and airspace management, with applications in airports, military installations, critical infrastructure, and public safety.

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Cite This Article

Vishnuvardhini P, Udhayakumar P, Praveen kumar S, Pradeep R, "AUTOMATIC DRONE DETECTION SYSTEM," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 12, Dec 2024. https://doi.org/10.55041/IJSREM39896

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Publication Date

12/24/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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