Inertial Sensor Bias Correction via State Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inertial sensors face limitations due to bias drift, accuracy issues, mounting, and calibration problems, leading to unbounded error growth when outside references like GPS are unavailable, restricting their applications in applications requiring precise velocity, position, and orientation measurements.
Innovation Solution
A method and system for tracking and updating bias in inertial sensors during periodic motion, using a state estimation algorithm that determines maximum bias drift and noise bands, allowing real-time or post-processing bias corrections without external references, by recognizing motionless periods and updating bias levels based on sensor noise characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If inertial sensors are used without external references, then device independence and portability are improved, but measurement precision deteriorates due to unbounded error growth from bias drift
Solution Approach 1:
The patent implements feedback by continuously monitoring sensor output during periodic motion and automatically adjusting bias estimates based on observed signal characteristics. The system detects when the sensor remains stationary (signal within noise band) and uses this information to update bias values, creating a closed-loop correction mechanism that maintains accuracy without external references.
Solution Approach 2:
The system performs self-calibration by using its own periodic motion characteristics to identify and correct bias drift. During rest periods within the periodic cycle, the sensor naturally returns to a known state, allowing the system to self-update bias estimates without requiring external calibration equipment or reference systems.
2Measurement precision
If bias is updated frequently to track drift, then measurement precision is improved, but reliability deteriorates due to noise interference causing false updates
Solution Approach 1:
The system applies partial action by updating bias only during specific portions of the periodic cycle when the sensor is stationary and the signal falls within the noise band. Rather than continuously updating bias, the system selectively updates only when conditions indicate genuine stationary periods, avoiding excessive updates that would introduce noise-related errors.
Solution Approach 2:
The system changes the parameter being monitored from raw sensor output to bias-corrected signal within a defined noise band. By transforming the measurement parameter and comparing against statistical noise characteristics rather than absolute thresholds, the system reliably distinguishes between actual stationary periods and noise fluctuations.
3Measurement precision
If complex filtering and noise analysis are applied, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system exploits the periodic nature of the sensor's motion cycle to simplify processing. By synchronizing bias updates with the known periodic rhythm and only analyzing signals during expected rest periods, the system avoids the need for continuous complex filtering and noise analysis, reducing computational complexity while maintaining precision.
Data Source
AI summary
A method and system for tracking and updating bias in an inertial sensor by determining a maximum bias drift and a noise band for a sensor, determining a prior bias value of the sensor, and measuring a current bias value of the sensor. The method and system can further include calculating a bias difference between the prior bias value and the current bias value, and updating the prior bias value with the current bias value if the current bias value is within the noise band and the bias difference is less than or equal to the maximum bias drift.