Extended Kalman Filter for Aircraft Flight Parameter Estimation
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Solution Overview
Problem
Modern aircraft rely heavily on accurate measurements of flight parameters, but external disturbances such as icing or sensor fouling can lead to erroneous values, causing failures in flight systems, especially when single sensor failures are not accounted for, resulting in unavailability of essential flight parameters.
Innovation Solution
Implementing an extended Kalman filter configured with flight mechanics equations that co-estimate at least two linked flight parameters, allowing for simultaneous estimation even when one or more input parameter values are erroneous, and adapting covariance matrices R and Q in real-time to accommodate sensor failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware redundancy is used to improve reliability of flight parameter measurements, then the availability of flight parameters improves, but the device complexity increases due to multiplying sensors and computers
Solution Approach 1:
The patent combines multiple flight parameters (airspeed, angle of incidence, altitude) into a single extended Kalman filter system that processes them jointly. This merging approach allows the system to estimate one parameter using information from others, reducing the need for separate redundant sensor systems while maintaining reliability.
Solution Approach 2:
The extended Kalman filter acts as an intermediary that processes and reconciles data from multiple sensors. When a sensor fails or provides erroneous data, the filter uses the mathematical models and other sensor data to compute reliable estimates, thereby mediating between faulty measurements and the need for accurate flight parameters.
2Reliability
If a single sensor failure occurs, then the measurement precision deteriorates, but the availability of flight parameters should be maintained through estimation methods
Solution Approach 1:
The extended Kalman filter continuously compares sensor measurements with predictions from flight mechanics models, using the difference (innovation) to update parameter estimates. This feedback mechanism allows the system to detect and compensate for sensor failures by relying more heavily on model-based predictions when measurements become unreliable.
Solution Approach 2:
The filter dynamically adjusts the weighting between sensor measurements and model predictions by changing the covariance parameters. When sensor data becomes unreliable, the filter increases the weight of model-based estimates, effectively changing parameters to maintain accuracy despite measurement degradation.
3Reliability
If Kalman filter estimation is used to maintain parameter availability, then the reliability improves, but the measurement precision deteriorates when input parameters are erroneous
Solution Approach 1:
The extended Kalman filter dynamically adapts its operation based on the quality of input measurements. It continuously adjusts the gain parameters and covariance matrices to optimize the balance between trusting sensor measurements and relying on model predictions, ensuring precision is maintained even when some inputs are erroneous.
Solution Approach 2:
The system creates a composite estimation approach by combining multiple flight parameters and their interrelationships into a unified estimation model. This composite approach allows the system to cross-validate measurements and maintain precision by using the consistency (or inconsistency) of multiple parameters to detect and compensate for errors.
Data Source
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AI summary
Method and system for determining flight parameters of an aircraft. According to the invention, the system for determining, in real time, the flight parameters of an aircraft, during a flight of the latter, comprises an extended Kalman filter (10) which is configured from equations of flight mechanics establishing a link of dependence between at least two pre-selected flight parameters of said aircraft to be estimated and which is formed to deliver, during a flight of said aircraft, joint estimates of said selected flight parameters.