Drone Location Detection Using Radio Condition Prediction
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Solution Overview
Problem
Drones in communications networks can intentionally or unintentionally report inaccurate locations, causing interference, violating flight regulations, or disrupting communication networks, due to factors like jammers or GNSS inaccuracies.
Innovation Solution
A method and system that determine the actual location of a drone by comparing measured radio conditions with predicted radio conditions using channel models and machine learning, enabling accurate detection of deviations from reported paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If drones report false locations to disrupt ground communication network, then interference in uplink increases, but network reliability deteriorates
Solution Approach 1:
The system uses feedback from multiple sources including reported location data, radio condition measurements, and predicted radio conditions to detect and respond to false location reports. The network continuously monitors for inconsistencies between reported and actual drone positions and adjusts accordingly to maintain reliability despite interference attempts
Solution Approach 2:
The system introduces an intermediary verification mechanism that mediates between drone location reports and network operations. By comparing reported locations with predicted radio conditions and actual measurements, the system creates a buffer layer that detects and mitigates false reports before they can cause harmful interference
2Object-generated harmful factors
If drones fly in no fly zones to disrupt communications, then network interference increases, but network reliability decreases
Solution Approach 1:
The system continuously monitors drone locations and radio conditions, providing feedback to detect when drones enter restricted zones. This enables real-time detection and response to prevent harmful interference in no-fly zones while maintaining network reliability
Solution Approach 2:
The system performs preliminary verification of drone locations against restricted zone databases before allowing operations. By checking reported locations against known no-fly zones in advance and continuously monitoring for deviations, the system prevents disruptive behavior before it can cause interference
3Strength
If drones use side or back lobes of BS antennas when above boresight, then antenna gain decreases, but signal quality deteriorates
Solution Approach 1:
The system dynamically adapts antenna configuration and beamforming parameters based on drone location and flight characteristics. When drones operate at altitudes where side/back lobes are used, the system adjusts transmission parameters to optimize signal quality despite the inherent reduction in antenna gain from main lobe operation
Solution Approach 2:
The system changes transmission parameters such as power levels, beamforming weights, and antenna selection based on the operational mode. When detecting that a drone is above boresight and using side/back lobes, the system modifies these parameters to compensate for reduced antenna gain and maintain acceptable signal quality
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise detection of drones' actual locations, preventing interference and compliance with flight regulations, and maintaining network connectivity.
Implementation Method 1
obtaining a measurement of radio conditions between the drone and a node in the telecommunications network
Implementation Method 2
predicting radio conditions at one or more locations related to the reported location of the drone
Data Source
AI summary
A computer implemented method in a communications network for determining location information about an actual location of a drone comprises obtaining a reported location of the drone at a first time point and obtaining a measurement of radio conditions between the drone and a node in the telecommunications network, at the first time point. The method then comprises predicting radio conditions at one or more locations related to the reported location of the drone, and determining the location information about the actual location of the drone based on the measured radio conditions and the predicted radio conditions.