Heading Reference Sensor Loss Compensation Using Bank Angle Estimation
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Solution Overview
Problem
Existing vehicle heading reference systems face inaccuracies due to the absence or failure of temperature and pressure sensors, which are crucial for calculating true airspeed and bank angle, leading to errors in navigation.
Innovation Solution
The method involves estimating true airspeed and static air temperature using alternative sensor measurements and dynamic models, refining these estimates through filtering techniques, and using Kalman filters to compensate for sensor failures, allowing the system to continue accurate navigation even without direct sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature and pressure sensors are used to calculate true airspeed and bank angle, then navigation accuracy is improved, but system reliability deteriorates when sensors fail or become unavailable
Solution Approach 1:
The system dynamically changes operational parameters by switching from direct sensor measurements to alternative calculation methods when sensor availability changes. When temperature or pressure sensors fail, the system transitions to using accelerometer data and dynamic models to estimate the required parameters, thereby maintaining navigation accuracy despite sensor failures
Solution Approach 2:
The system introduces intermediary calculation methods as mediators between sensor failures and navigation accuracy. Instead of directly relying on failed sensors, the system uses intermediate variables (such as accelerometer measurements and dynamic models) to bridge the gap and maintain accurate navigation calculations
2Measurement precision
If redundant measurements from vehicle probes are used to correct gyro bias, then measurement accuracy is improved, but device complexity increases due to additional sensors and calibration requirements
Solution Approach 1:
The system makes existing sensors multi-functional by using accelerometers not only for their primary function but also for calculating bank angle and correcting gyro bias when temperature or pressure sensors are unavailable. This universal approach allows one sensor to perform multiple measurement functions, reducing the need for additional dedicated sensors
Solution Approach 2:
The system enables self-service by using its own existing sensor data (accelerometers, gyroscopes) to compensate for missing sensor measurements. The system calculates its own correction factors and performs self-calibration without requiring external redundant sensors, thereby maintaining measurement accuracy while avoiding additional hardware complexity
3Device complexity
If standard atmosphere models are used to estimate temperature, then system simplicity is improved, but measurement precision deteriorates when actual conditions deviate from standard atmosphere
Solution Approach 1:
The system implements feedback by continuously comparing standard atmosphere model predictions with actual sensor measurements when available. When temperature or pressure sensors are operational, their readings provide feedback to validate and adjust the standard atmosphere model, ensuring temperature estimation accuracy even when conditions deviate from standard atmosphere
Data Source
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AI summary
Methods and systems for compensating for the absence or loss of a sensor measurement in a heading reference system such as an aircraft attitude and heading reference system, integrated standby unit, or vehicle inertial system, provides an estimate of the lost sensor measurement by estimating the bank angle after a detected vehicle turn. The estimate of the bank angle may also be used to estimate the vehicle's speed. Additionally, when the lost sensor measurement is a temperature measurement, the described methods and systems offer an improvement over estimating air temperature using a standard (e.g., ISA) model. The methods and systems also allow for the refinement of computed estimates using filtering techniques, such as low-pass or Kalman filtering. The methods may be iteratively repeated for each detected turn in order to maintain an accurate estimate of the lost sensor measurement or other estimates, such as vehicle speed.