Method for drone authentication using real-time current consumption and computer device therefor
The drone authentication method using real-time current consumption data from brushless motors, analyzed by AI, addresses hardware-related issues and computing overhead, improving security and efficiency in drone identification.
KR102998971B1Active Publication Date: 2026-08-03IND UNIV COOP FOUND HANYANG UNIV ERICA CAMPUS
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Patent Information
- Application Number
- KR1020240200239
- Authority / Receiving Office
- KR · KR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2026-08-03
- Estimated Expiration
- 2044-12-30
AI Technical Summary
Technical Problem
Existing drone authentication methods require additional hardware, which increases weight, degrades flight performance, and incurs maintenance costs, while complex data exchange overloads computing resources and reduces reliability.
Method used
A drone authentication method using real-time current consumption data from the brushless motor, analyzed by a pre-trained artificial intelligence learning model, such as a Random Forest, to identify and authenticate drones without additional hardware.
Benefits of technology
This method enhances security and efficiency by reducing hardware requirements, minimizing computing load, and enabling faster, more accurate drone identification and management.
✦ Generated by Eureka AI based on patent content.
Abstract
According to an embodiment of the present application, a drone certification method using real-time current consumption and a computer device for the same are provided. The method may include the steps of: collecting real-time current consumption data of a drone to be certified for a predetermined period of time in a predetermined operating state; generating a characteristic vector related to the current consumption characteristics of the drone to be certified based on the real-time current consumption data; and inputting the characteristic vector into a pre-trained artificial intelligence learning model to determine whether the drone to be certified is a legal drone.
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