Probabilistic Trajectory Spacing for Mixed PBN and Non-PBN Fleets
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Solution Overview
Problem
Current air traffic management systems face challenges in coordinating flight paths for mixed fleets of Performance Based Navigation (PBN) and non-PBN aircraft, leading to inefficient use of capabilities and increased vectoring by air traffic controllers, which can result in longer flight times, delays, and higher fuel burn due to the lack of predictive trajectory planning and spacing coordination.
Innovation Solution
A system that generates flight path trajectories based on probabilistic predictions, using a probabilistic spacing advisory tool to determine optimal target spacings and required times of arrival for both PBN and non-PBN aircraft, optimizing sequencing and spacing to enhance the execution of RNP/RNAV approaches and reduce flight time, delay, and fuel burn.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional radio navigation beacons are used for flight paths, then compatibility with all aircraft types is maintained, but flight routes become less direct and less efficient
Solution Approach 1:
The system segments the mixed fleet into PBN-capable aircraft and non-PBN aircraft, applying different navigation approaches to each segment. PBN aircraft use performance-based navigation for direct routes while non-PBN aircraft follow conventional beacon-based routes, allowing both segments to operate efficiently without forcing compatibility changes on any single group
Solution Approach 2:
The system introduces probabilistic trajectory predictions and spacing advisories as intermediary elements that bridge PBN and non-PBN aircraft. These probabilistic models provide a common framework for trajectory management that accommodates both navigation capabilities, enabling optimized routing for the mixed fleet without requiring all aircraft to use the same navigation method
2Reliability
If air traffic controllers vector aircraft to maintain safe separation, then safety is ensured, but flight time and fuel burn increase
Solution Approach 1:
The system performs preliminary trajectory predictions and spacing calculations before aircraft reach critical separation points. By pre-calculating probabilistic trajectories and required spacing based on predicted arrival times and aircraft performance, controllers can establish optimal spacing in advance rather than reacting to real-time positions, reducing the need for last-minute vectoring and associated time losses
Solution Approach 2:
The system implements continuous feedback loops that monitor actual aircraft positions, speeds, and trajectories against predicted values. This feedback enables dynamic adjustment of spacing advisories and trajectory corrections, allowing the system to maintain safety while minimizing deviations from optimal routes. The feedback mechanism ensures that spacing requirements are met without unnecessary vectoring maneuvers
3Manufacturing precision
If RNP/RNAV procedures are developed for destination terminal areas, then navigation precision is improved, but utilization is reduced due to aircraft capability restrictions
Solution Approach 1:
The system creates a universal trajectory management framework that serves both PBN and non-PBN aircraft. By using probabilistic predictions and spacing advisories as common language, the system enables RNP/RNAV procedures to be utilized by PBN aircraft while maintaining compatibility with non-PBN aircraft operations. This multi-functional approach increases overall productivity by allowing more aircraft to benefit from precision navigation procedures without excluding any capability group
4Loss of time
If probabilistic trajectory predictions are used for sequencing and spacing, then flight time and delay are reduced, but system complexity increases
Solution Approach 1:
The system transforms deterministic trajectory planning into probabilistic parameter-based planning. By using predicted arrival times, probabilistic spacing factors, and performance-based parameters rather than fixed beacon positions, the system achieves more flexible and time-efficient routing. This parameter transformation allows the system to handle variability in aircraft performance and navigation capabilities without requiring complex real-time calculations for each individual aircraft
Data Source
AI summary
A method, medium, and system to receive flight parameter data relating to a plurality of flights, the flight parameter data including indications of aircraft performance based navigation (PBN) capabilities, flight plan information, an aircraft configuration, and an airport configuration for the plurality of flights; assign probabilistic properties to the flight parameter data; receive accurate and current position and predicted flight plan information for a plurality of aircraft corresponding to the flight parameter data; determine a probabilistic trajectory for two of the plurality of aircraft based on a combination of the probabilistic properties of the flight parameter data and the position and predicted flight plan information, the probabilistic trajectory being specific to the two aircraft and including a target spacing specification to maintain a predetermined spacing between the two aircraft at a target location with a specified probability; and generate a record of the probabilistic trajectory for the two aircraft.