Drone Detection Through QAM Order for Remote-Control Distance
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Solution Overview
Problem
Existing methods for detecting drones are inaccurate and unreliable due to encrypted signals, making it difficult to determine their presence and distance from their remote controls, especially in the presence of spoofing, and lack effective defense mechanisms.
Innovation Solution
A method using machine learning models, particularly support vector machines (SVMs), to analyze drone signals without decryption, identifying drone-specific patterns in radio signals and determining distance based on quadrature amplitude modulation (QAM) order, enabling reliable detection and distance measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If radar is used to detect drones, then detection coverage is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces radar (electromagnetic wave-based mechanical detection system) with an AI-based signal analysis system that processes radio frequency signals. The AI model analyzes signal characteristics, modulation patterns, and spectral features to detect drones, achieving both wide coverage and high precision without the limitations of radar.
Solution Approach 2:
The patent changes the detection parameters from radar-based physical measurements to signal-based parameters including frequency spectrum analysis, modulation detection, and signal strength characteristics. By analyzing multiple signal parameters simultaneously through AI, the system achieves accurate drone detection and distance measurement.
2Reliability
If signal encryption is used by drones, then communication security is improved, but reliability of detection deteriorates
Solution Approach 1:
The patent extracts and analyzes specific characteristics from encrypted drone signals without attempting to decrypt the content. The AI model extracts features such as modulation patterns, frequency hopping sequences, signal strength variations, and temporal characteristics that uniquely identify drone communications, enabling detection despite encryption.
Solution Approach 2:
Instead of trying to break through the encryption to access signal content, the patent inverts the approach by analyzing the encryption itself as a detection feature. The AI model uses the presence and characteristics of encrypted signals as positive indicators of drone presence, turning the security measure into a detection opportunity.
3Reliability
If spoofing is used to protect remote control, then position security is improved, but accuracy of distance determination deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the AI model continuously analyzes signal characteristics and compares them against known spoofing patterns. The system provides feedback by identifying inconsistencies between transmitted and received signal properties, detecting spoofing attempts, and adjusting distance calculations based on the authenticity assessment of the signals.
Solution Approach 2:
The patent performs preliminary analysis of signal characteristics before final distance determination. The AI model pre-processes signals to identify authentic drone-control communications versus spoofed signals by examining modulation consistency, frequency stability, and temporal patterns, ensuring accurate distance measurement only from verified authentic signals.
Data Source
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AI summary
The invention relates to a method for detecting drones and determining the distance to their remote control (114), comprising the steps of: - detecting a plurality of wirelessly transmitted radio signals (200) through the air using a detection device; - checking for the presence of at least one drone (100) within the range of the detection device by analyzing the radio signals (200); - detecting or determining an order (QAM order) of a quadrature amplitude modulation (QAM) used in the transmission of the radio signals (200) of the drone (100); and - determining the distance of the drone (100) from its associated remote control (114) based on the order of the quadrature amplitude modulation. The invention also relates to a computer program and a detection device for carrying out the method.