Smartphone Bracket Car Navigation MEMS Sensor Calibration
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Solution Overview
Problem
Existing GPS+IMU systems for car and truck navigation face challenges due to errors from low-cost MEMS sensors, particularly milliradian level errors in gyros leading to positioning inaccuracies, and the need for self-calibration without precise alignment of sensors with vehicle axes.
Innovation Solution
A cordless GPS+IMU system utilizing MEMS pitch rate and yaw rate gyros, a longitudinal accelerometer, and an altimeter, combined with self-calibration methods like blind and delta-V calibration, and a Kalman filter to estimate position, heading, and speed, while ignoring lateral and vertical acceleration measurements to reduce error accumulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-cost MEMS sensors are used in GPS+IMU systems, then system cost is reduced, but positioning accuracy deteriorates due to milliradian level errors in gyros
Solution Approach 1:
The system performs self-calibration by detecting when the vehicle is stationary and using gravity vector measurements to determine the correct orientation of the IMU sensors relative to the vehicle axes. This eliminates the need for precise manual alignment during installation while compensating for sensor errors through automated calibration procedures.
Solution Approach 2:
The system changes the operational parameters of the low-cost MEMS sensors through calibration factors and correction algorithms. By adjusting sensor output parameters based on calibration data collected during stationary periods, the system compensates for inherent measurement errors and achieves acceptable positioning accuracy.
2Measurement precision
If precise alignment of sensors with vehicle axes is required, then measurement accuracy is improved, but installation complexity and time increase
Solution Approach 1:
The system performs self-calibration by detecting when the vehicle is stationary and using gravity vector measurements to determine the correct orientation of the IMU sensors relative to the vehicle axes. This eliminates the need for precise manual alignment during installation while compensating for sensor errors through automated calibration procedures.
Solution Approach 2:
The system performs calibration actions during stationary periods before normal navigation begins. By pre-determining the relationship between sensor axes and vehicle axes during installation and after, the system eliminates the need for precise real-time alignment while maintaining measurement accuracy.
3Loss of information
If all sensor measurements including lateral and vertical acceleration are used, then navigation information completeness is improved, but error accumulation increases
Solution Approach 1:
The system extracts and uses only the relevant longitudinal acceleration component for dead reckoning navigation, discarding lateral and vertical acceleration measurements that would contribute to error accumulation. This selective approach maintains sufficient navigation information while eliminating sources of error.
Solution Approach 2:
The system applies different processing quality to different sensor measurements: longitudinal acceleration is processed with high precision for position estimation, while lateral and vertical components are either discarded or processed with lower priority, optimizing overall system reliability by focusing computational resources on the most critical navigation parameters.
Data Source
AI summary
Inertial navigation systems for wheeled vehicles with constrained motion degrees of freedom are described. Various parts of the navigation systems may be implemented in a smart-phone.