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 failing to provide sufficient information for end users.
Innovation Solution
A system and method that utilize a phase determination control unit to receive position data from an aircraft, determine relevant variables, apply fuzzy logic to score possible phases, and identify the highest score as the actual phase of flight, while also analyzing additional variables such as airport locations and air traffic structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fuzzy logic is applied to determine likelihoods for phase identification, then the system can handle uncertainty in flight data, but the method remains rudimentary and lacks robustness
Solution Approach 1:
The system segments the phase determination process into multiple independent modules: a monitoring subsystem that collects raw flight data, a phase determination control unit that processes the data through fuzzy logic, and a database that stores phase information. This segmentation allows each module to be optimized independently, improving overall reliability without proportionally increasing complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer between data collection and phase identification. The phase determination control unit acts as a mediator that receives raw position data, applies fuzzy logic algorithms, and produces refined phase determinations. This intermediary layer enhances robustness by isolating the complex fuzzy logic operations from the simple data collection and display functions.
2Loss of information
If only four phases are identified by the known method, then the determination process is simple, but insufficient information is provided to end users
Solution Approach 1:
The system dynamically adjusts the level of phase granularity based on flight conditions and user needs. During critical phases like approach and landing, the system provides more detailed phase information (e.g., distinguishing between final approach, missed approach, and go-around). This dynamic adaptation increases information completeness without requiring the system to maintain maximum complexity throughout all flight conditions.
Solution Approach 2:
The patent changes the parameter of phase classification from a fixed four-phase model to a variable multi-phase model. The system can identify numerous phases including ground, climb, cruise, descent, approach, landing, and various transitional phases. This parameter change allows the system to provide comprehensive information to end users while managing complexity through selective activation of phase identification algorithms.
3Measurement precision
If enroute climb events are automatically classed as climb, then processing is efficient, but accuracy is reduced due to lack of differentiation
Solution Approach 1:
The system applies partial differentiation to phase classification. Rather than fully analyzing every single phase transition with complex algorithms, the system uses simplified rules for obvious cases (automatic classification of clear climb or descent events) and reserves detailed analysis for ambiguous situations. This partial action approach maintains high processing efficiency while improving accuracy where it matters most.
Solution Approach 2:
The patent applies different levels of analysis quality to different flight phases based on their importance and ambiguity. Critical phases like approach and landing receive detailed local analysis with multiple variables and fuzzy logic rules, while routine phases like cruise use simpler classification. This local quality differentiation improves measurement precision for important phases without sacrificing overall processing efficiency.
Data Source
AI summary
A system and a method include a phase determination control unit configured to receive position data of an aircraft, determine variables from messages received from the aircraft, 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.