Drone Detection via Base Station Beam Power Analysis
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Solution Overview
Problem
Current methods for detecting drones using cellular communication networks face challenges such as interference and unsatisfactory detection accuracy, especially in multipath propagation scenarios, and fail to distinguish between drones at close positions.
Innovation Solution
A method utilizing control signals transmitted by user equipment embedded in drones, processed by base stations with multiple receive antennas to identify the receive beam with the highest power, determining the drone's altitude and presence of vibrations, allowing for accurate detection regardless of altitude, and enabling communication management decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drone detection is performed using existing RF-based localization methods, then drone location can be identified, but detection accuracy is unsatisfactory especially in multipath propagation scenarios
Solution Approach 1:
The base station segments the detection task by dividing the antenna array into multiple sub-arrays, each forming independent receive beams. This segmentation allows the system to process signals from different spatial directions separately, improving accuracy in multipath environments where signals arrive from multiple paths. The method segments the spatial spectrum into discrete angular intervals and processes each interval independently.
Solution Approach 2:
The invention transforms the detection problem from traditional RF signal analysis into a spatial domain problem by introducing angular dimension. Instead of analyzing only frequency and time dimensions, the system adds spatial dimension through receive beams formed by antenna arrays, creating a three-dimensional detection space (frequency-time-angle) that effectively separates direct signals from multipath reflections.
2Measurement precision
If traditional detection methods are used, then some drone detection capability is achieved, but the system cannot distinguish between drones at close positions
Solution Approach 1:
The system recovers spatial information by transforming signal detection into the angular domain. Each receive beam corresponds to a specific angular interval, allowing the system to distinguish drones at close positions based on their different spatial angles. This adds a spatial dimension to the detection capability, enabling resolution of closely spaced drones that traditional methods cannot separate.
Solution Approach 2:
The invention applies local quality by assigning different detection characteristics to different angular regions. Each receive beam is optimized for its specific angular interval, with dedicated processing parameters tailored to local signal characteristics. This allows the system to maintain high detection precision for each individual drone while preserving the ability to distinguish between multiple drones in different spatial locations.
3Extent of automation
If user equipment identifies itself as drone-mounted, then network access control is enabled, but some user equipment does not identify themselves, requiring alternative detection methods
Solution Approach 1:
The system implements self-service detection by having the user equipment's transmitted signals carry inherent spatial information that automatically reveals drone-mounted status. The base station processes these signals through angular spectrum analysis, and the spatial characteristics themselves indicate whether the equipment is drone-mounted, eliminating the need for explicit identification messages from the user equipment.
Solution Approach 2:
The detection method serves multiple functions simultaneously: it provides automatic drone detection, maintains compatibility with both identifying and non-identifying user equipment, preserves network access control capabilities, and enables spatial discrimination. This universal approach works for all user equipment types without requiring additional complexity in the user equipment itself.
Data Source
AI summary
The development relates to the detection of drones. The use of a cellular communication network by drones may cause problems because of interference generated by a user device located on-board a drone flying higher than antennas of the base stations. It is important, for a telecommunications operator, to be able to control use of the cellular communication network by drones. Methods allowing drones to be detected exist. These methods, although they allow a location of a drone to be determined, do not provide a satisfactory detection accuracy. Likewise, they do not allow two drones in similar positions to be distinguished between. The method is based on the use of control signals the characteristics of which are known and on the use of known properties of the transmission channel set up between the base station and the user device, to determine an altitude value that is accurate and reliable.