3D UAV Network Coverage Modeling for BVLOS Guidance
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Solution Overview
Problem
Conventional radio network planning techniques are inadequate for managing unmanned aerial vehicle (UAV) traffic, particularly in three-dimensional airspaces, as they fail to provide real-time network coverage and connectivity data essential for safe beyond-visual-line-of-sight operations, lacking interaction with aviation control systems and relying on two-dimensional models.
Innovation Solution
An apparatus and method for computing three-dimensional (3D) network coverage data by correlating stored network data with current connectivity data, including location and event information, to facilitate safe UAV operations by determining handover probabilities and signal power, enabling efficient flight path planning and air traffic control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional two-dimensional radio network planning techniques are used, then device complexity is reduced, but network coverage accuracy and reliability in three-dimensional airspace deteriorate
Solution Approach 1:
The patent transitions from conventional two-dimensional radio network planning to three-dimensional network coverage modeling by incorporating altitude as an additional dimension. The system creates 3D coverage maps that account for vertical signal propagation characteristics, enabling accurate representation of network coverage in volumetric airspace rather than flat geographic areas.
Solution Approach 2:
The system implements dynamic network coverage assessment by continuously updating 3D coverage models with real-time network data, UAV positions, and environmental conditions. The coverage predictions are refreshed periodically or triggered by events such as UAV entry into new airspace volumes, ensuring current and accurate guidance information.
2Reliability
If real-time network coverage data is provided for BVLOS operations, then safety and reliability of UAV operations improve, but data processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary network coverage assessments and pre-computes 3D coverage models for planned UAV flight areas before actual operations. By establishing baseline coverage maps and identifying potential connectivity issues in advance, the system reduces the computational burden during active UAV operations and enables proactive mitigation strategies.
Solution Approach 2:
The system implements feedback mechanisms where actual network measurements from UAVs and ground-based sensors are continuously compared against predicted 3D coverage models. Discrepancies trigger model updates and refinements, creating a self-correcting system that improves accuracy over time while maintaining computational efficiency through adaptive learning.
3Loss of information
If three-dimensional network coverage modeling is implemented, then loss of information about vertical signal propagation is reduced, but measurement and computation difficulty increase
Solution Approach 1:
The system introduces intermediary elements such as elevated reference antennas, rooftop measurement points, and atmospheric sensing devices that facilitate the collection of vertical propagation data without requiring direct measurements from all possible UAV positions. These intermediaries act as proxies that capture three-dimensional signal characteristics more easily than direct UAV-based measurements.
Solution Approach 2:
The system replaces complex physical measurement systems with computational modeling approaches. Instead of deploying extensive sensor networks throughout the three-dimensional space, the system uses electromagnetic field theory-based propagation models that calculate signal behavior mathematically, substituting mechanical measurement complexity with computational algorithms.
Data Source
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AI summary
The present disclosure provides an apparatus and a method for computing data for guiding unmanned aerial vehicles (UAVs) in a three-dimensional (3D) air space. Stored network data including location data of network nodes as well as current network data including current connectivity data of the network nodes are acquired. By correlating the stored and current network data, current 3D coverage data indicating current network coverage along three dimensions in the 3D flight area are determined. The techniques of the present disclosure enable modeling and predicting a current coverage and connectivity state of 3D air space and facilitates control and operation of UAV air traffic.