Real-Time Cellular Detection of Unauthorized Drones
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to effectively distinguish and identify unauthorized cellular drones, particularly those using consumer SIM cards, as they blend in with legitimate drones and terrestrial users, complicating detection in restricted zones.
Innovation Solution
A method involving cellular network measurements to identify drones by analyzing received signal power, interference patterns, and SIM card types, combined with detachment and reattachment procedures to confirm drone identity, and reconstructing flight paths for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If cellular network measurements are used to detect drones, then detection capability is improved, but false identification of legitimate drones increases
Solution Approach 1:
The detection process is divided into multiple independent measurement phases: initial detection using received signal power, secondary verification using interference pattern analysis, and final confirmation using detachment/reattachment procedures. Each phase segments the detection task to reduce false positives while maintaining high detection capability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring interference patterns and signal power changes during detachment and reattachment procedures. This feedback loop allows the system to verify initial detections and correct false identifications through iterative measurement and comparison against established thresholds.
2Measurement precision
If multiple measurement procedures are implemented, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The cellular network infrastructure is leveraged to perform multiple detection functions simultaneously: existing base stations measure both received signal power and interference patterns using their standard communication protocols. This multi-functionality approach eliminates the need for dedicated detection hardware, reducing system complexity while maintaining high measurement precision.
Solution Approach 2:
The system uses the cellular network's own operational parameters (signal power, interference patterns, attachment/detachment procedures) as the measurement basis. This self-service approach leverages existing network resources rather than requiring external complex measurement systems, thereby improving accuracy without proportionally increasing complexity.
3Speed
If real-time detection is implemented, then response time is improved, but processing requirements increase
Solution Approach 1:
The system performs partial measurement actions only when necessary: initial received signal power measurement is conducted continuously, but full interference pattern analysis and detachment/reattachment procedures are executed only when initial detection thresholds are met. This partial action approach enables real-time response while reducing overall processing requirements by avoiding exhaustive measurements for all devices.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, identifying user equipment (UE) as cellular drones. A list of suspected cellular drones may be generated based on criteria such as received power levels, changes in interference levels when UEs detach from serving cells and attach to neighboring cells, and the like. Suspected cellular drones may be identified as legitimate cellular drones or nonlegitimate cellular drones based on criteria such as the type of subscriber identity module (SIM) card used. Other embodiments are disclosed.