Aircraft Intent Inference Using Evolutionary Algorithms
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Solution Overview
Problem
Current methods lack an efficient way to infer an aircraft's intent from its observed trajectory without requiring special airborne equipment or additional data communication infrastructure, making it difficult to predict future trajectories for conflict resolution and air traffic management analysis.
Innovation Solution
A computer-implemented method using evolutionary algorithms to infer aircraft intent from observed trajectories, leveraging radar and ADS data, atmospheric conditions, and aircraft performance data, without active collaboration from the aircraft, and expressing the intent in a formal language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If aircraft intent data is provided by the aircraft or operator, then the intent information is available, but special airborne equipment or additional data communication infrastructure is required
Solution Approach 1:
The system uses existing airborne equipment (radar transponders and ADS-B transmitters) that aircraft already carry for other purposes. These existing systems provide the trajectory data needed for intent inference without requiring any new equipment or dedicated communication infrastructure for intent data transmission.
Solution Approach 2:
The patent introduces an intermediary processing system that infers aircraft intent from publicly available trajectory data. This intermediary layer translates existing radar and ADS-B position data into meaningful intent information, avoiding the need for direct intent data transmission from aircraft.
2Measurement precision
If aircraft intent is inferred using evolutionary algorithms with multiple parameters, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the aircraft intent inference problem into distinct components: horizontal motion parameters, vertical motion parameters, and flight segment identification. This segmentation allows the evolutionary algorithm to optimize each aspect separately, managing computational complexity while maintaining overall prediction accuracy.
Solution Approach 2:
The system uses evolutionary algorithms to dynamically adjust and optimize multiple parameters including flight segment boundaries, horizontal/vertical motion characteristics, and trajectory predictions. By changing and optimizing these parameters iteratively, the system achieves high prediction accuracy while the modular parameter structure helps manage computational complexity.
Data Source
AI summary
A method is provided for inferring the aircraft intent of an aircraft from an observed trajectory. Aircraft performance data relating to that type of aircraft is retrieved from memory, along with atmospheric conditions along the observed trajectory. An initial set of candidate aircraft intents is generated. Each aircraft intent provides an unambiguous description of how the aircraft may be flown that allows a determination of an unambiguous resulting trajectory. A computer system calculates a trajectory defined by each candidate aircraft intent and forms a cost function from a comparison of each calculated trajectory to the observed trajectory. An evolutionary algorithm evolves the initial candidate aircraft intents, wherein the evolutionary algorithm uses a multi-objective cost function to obtain a cost function value that measures the suitability of each candidate aircraft intent.