Aircraft Phase Determination Using Fuzzy Logic
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Solution Overview
Problem
Existing methods for determining the phase of flight of an aircraft are rudimentary and lack robustness, only identifying four phases and struggling to differentiate between more detailed phases, such as initial climb and enroute climb initial.
Innovation Solution
A system and method using a phase determination control unit that receives position data including height, altitude, and distance from locations, applies fuzzy logic to determine scores for possible phases, and identifies the highest score as the actual phase of flight, allowing differentiation between multiple phases like initial climb, enroute climb initial, and cruise based on altitude and distance from departure locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fuzzy logic is applied to determine phases of flight, then the robustness and accuracy of phase determination is improved, but the computational complexity and processing time increases
Solution Approach 1:
The phase determination process is segmented into distinct evaluation stages: data collection from multiple sources (GPS, barometer, accelerometer), variable extraction (altitude, speed, position, orientation), fuzzy logic rule evaluation for each phase, and score aggregation. This segmentation allows the complex fuzzy logic system to process information in manageable stages, improving reliability without overwhelming computational resources.
Solution Approach 2:
The system dynamically adjusts the evaluation process by continuously receiving real-time data and recalculating phase scores based on current flight conditions. The fuzzy logic rules are dynamically applied to changing variables such as altitude, speed, and position, allowing the system to adapt to varying flight scenarios while maintaining computational efficiency through incremental updates rather than complete recalculations.
2Loss of information
If multiple phases of flight are identified, then the information detail and differentiation capability is improved, but the complexity of phase classification and determination increases
Solution Approach 1:
The system distinguishes between vertical dimension phases (takeoff, climb, cruise, descent, landing based on altitude changes) and horizontal dimension phases (taxi, takeoff roll, landing roll based on ground position and movement). This multi-dimensional classification approach enables detailed phase identification without requiring a single complex classification system, as each dimension has its own set of simpler rules that work together.
Solution Approach 2:
Different phases are identified by monitoring changes in key parameters: altitude (for vertical phases), ground speed (for horizontal phases), and position relative to airports (for transition phases). The fuzzy logic system evaluates multiple parameter thresholds simultaneously, allowing detailed phase differentiation through parameter-based rules rather than complex hierarchical classification.
3Measurement precision
If fuzzy logic with multiple variables is used to differentiate phases, then the measurement precision and phase differentiation accuracy is improved, but the data processing requirements and computational load increases
Solution Approach 1:
The system performs preliminary data validation and filtering before applying fuzzy logic rules, ensuring that only relevant and reliable data enters the computational process. Variables are pre-processed to extract meaningful features (such as rate of climb from altitude changes, or ground speed from position data), reducing the computational burden during the actual phase determination while maintaining high differentiation accuracy.
Solution Approach 2:
The fuzzy logic system uses standardized rule templates that can be copied and applied across different phase evaluations. Once rules are established for distinguishing phases based on variable combinations, these rule sets can be reused and adapted for different flight scenarios, reducing computational energy by avoiding redundant rule development and enabling efficient processing through template matching.
Data Source
AI summary
A system and method include a phase determination control unit configured to receive position data of an aircraft. The position data includes height of the aircraft, altitude of the aircraft, and distance of the aircraft from one or more locations. The phase determination control unit is further configured to determine variables from messages received from the aircraft. The variables relate to the height of the aircraft, the altitude of the aircraft, and the distance of the aircraft from the one or more locations. The phase determination control unit is further configured to apply fuzzy logic to the variables to determine scores for possible phases of flight of the aircraft, identify a highest score among the possible phases of flight, and determine the highest score as an actual phase of flight of the aircraft.