Aircraft Flight Path Planning for Radio Connectivity Coverage
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Solution Overview
Problem
Current airborne communication systems face challenges in generating efficient flight paths for aerial vehicles carrying communication hubs due to the complexity of interacting variables such as the number of communication nodes, their priorities, and environmental factors like terrain and weather, which requires multi-disciplinary expertise and often results in approximate solutions.
Innovation Solution
A method is developed to model geographic space and time, including mobile communication nodes, to generate flight paths that provide desired connectivity by simulating various paths and dynamically updating the flight path based on real-time sensor data, allowing for automated planning and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual flight path planning is used by mission planners, then flight paths can be generated with human expertise, but the complexity increases combinatorially with the number of communication nodes and their requirements
Solution Approach 1:
The system enables automated flight path planning where the computer system independently generates, evaluates, and optimizes flight paths without requiring human mission planners to manually process the combinatorial complexity of multiple communication nodes, their priorities, and environmental constraints
Solution Approach 2:
The patent replaces the mechanical human planning process with an automated computational system that uses algorithms to evaluate flight paths based on connectivity metrics, substituting human expertise with machine-based optimization
2Quantity of substance
If the number of communication subscriber nodes and their mission priorities increase, then communication coverage improves, but the combinatorial explosion of interacting variables makes planning increasingly difficult
Solution Approach 1:
The system transforms the planning problem by changing parameters from manual waypoint entry to automated optimization based on connectivity metrics, allowing the system to handle increasing numbers of communication nodes by algorithmically evaluating their spatial distribution and communication requirements
3Adaptability or versatility
If sophisticated non-deterministic algorithms are used to find approximate solutions, then flight paths can be generated for complex scenarios, but the computational complexity and time increase
Solution Approach 1:
The system implements feedback mechanisms where flight paths are evaluated based on actual connectivity metrics achieved, allowing iterative optimization that converges on effective solutions more efficiently than exhaustive search methods
4Ease of operation
If mission planners enter waypoints manually, then flight paths can be specified, but it is hard to measure or predict the effectiveness of the path and precisely when subscribers can expect connectivity
Solution Approach 1:
The system performs preliminary evaluation of flight paths by simulating connectivity before actual execution, allowing prediction of when subscribers will achieve connectivity and enabling pre-optimization of routes based on anticipated performance
Data Source
AI summary
A method of generating a flight path for an aircraft is provided. The method includes modeling geographic space and time that includes a plurality of mobile communication nodes. The model includes locations of each of the plurality of mobile communication nodes as those nodes move over time. The model also provides an indication of wireless connectivity between a radio on each of the plurality of communication nodes and a radio of the aircraft at their respective location. The method further includes running a plurality of flight paths through the model in order to identify a selected flight path that provides a desired level of connectivity between the aircraft and the plurality of communication nodes.