Vertical Reference Unit Gyroscope Bias Drift Correction
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Solution Overview
Problem
Inertial measurement units (IMUs) used in vertical reference units (VRUs) suffer from unreferenced heading drift errors due to unstable gyroscope bias, which is affected by temperature and operation time, leading to inaccurate yaw data and unacceptable drift over time.
Innovation Solution
A circulation bias cancelation (CBC) algorithm is implemented, which uses processor modules to read IMU data during start-up and in-run operations to detect static conditions and correct gyroscope bias by subtracting initial and current bias values, reducing heading drift and improving yaw performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory calibration is used to reduce gyroscope bias, then initial accuracy is improved, but bias drift over time and temperature remains
Solution Approach 1:
The system performs preliminary static detection and bias correction during start-up before normal operation begins. The first bias cancelation module detects static conditions during start-up and corrects gyroscope bias by subtracting initial bias values, preparing the system in advance to prevent drift accumulation during subsequent dynamic operation.
Solution Approach 2:
The system continuously monitors gyroscope output and implements feedback-based bias correction. The second bias cancelation module detects static conditions during in-run operation and adjusts bias by subtracting current bias values from previous values, creating a closed-loop system that automatically compensates for drift over time and temperature changes.
2Reliability
If static detection is implemented to correct bias, then heading drift is reduced, but system complexity increases
Solution Approach 1:
The bias cancelation functionality is divided into two independent modules: the first bias cancelation module for start-up operation and the second bias cancelation module for in-run operation. Each module has dedicated static detection logic and bias correction algorithms, allowing independent optimization and simplifying debugging while achieving comprehensive drift reduction across all operational phases.
Solution Approach 2:
The system uses its own gyroscope data to detect static conditions and determine bias values without requiring external reference instruments or additional sensors. The gyroscope output itself serves as the input for static detection and bias calculation, making the system self-sufficient and avoiding added hardware complexity.
3Measurement precision
If continuous bias correction is applied, then yaw accuracy is improved, but computational load increases
Solution Approach 1:
Instead of continuous bias correction, the system performs bias correction periodically whenever static conditions are detected. The static detector monitors gyroscope output and triggers bias correction only when the vehicle is stationary, converting continuous computational effort into periodic operations that reduce processor energy consumption while maintaining accuracy during critical static periods.
Solution Approach 2:
The bias correction frequency and intensity adapt dynamically based on operational conditions. During static periods, full bias correction is applied; during dynamic operation, the system relies on the previously corrected bias values. This dynamic approach optimizes computational resources by applying intensive processing only when necessary for accuracy.
Data Source
AI summary
A system to reduce VRU unreferenced heading drift error is disclosed. The VRU comprises an IMU and a processor, which hosts first and second modules for bias cancelation. The first module reads inertial data from the IMU when the VRU is powered on; determines whether the VRU is static for a time period; if the VRU is static, corrects gyroscope bias by subtracting an initial bias value from a previous bias value; sets a predefined initial yaw value. The second module reads inertial data from the IMU during in-run operation of the VRU; updates roll, pitch and yaw data, based on input data from a sensor fusion algorithm; outputs updated roll, pitch and yaw data; determines whether the VRU is static for a time period; and if the VRU is static, corrects the bias by subtracting a current bias value, multiplied by a predefined parameter, from a previous bias value.