Pre-calibration method for pressure drift of MEMS sensor based on outdoor cold environment
By employing a natural air pressure recovery and nonlinear correction model based on an outdoor cold environment during MEMS sensor calibration, the vibration noise interference and non-natural issues in MEMS sensor air pressure drift calibration are resolved. This achieves efficient and low-cost air pressure drift correction, making it suitable for high-precision applications of MEMS sensors in real-world environments.
CN121804751BActive Publication Date: 2026-06-09SHENZHEN BEIDOU COMM TECH CO
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN BEIDOU COMM TECH CO
- Filing Date
- 2026-03-06
- Publication Date
- 2026-06-09
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Figure CN121804751B_ABST
Abstract
The application relates to the field of MEMS sensors and discloses a MEMS sensor barometric pressure drift pre-calibration method based on an outdoor cold environment, which comprises the following steps: S1, low-pressure environment construction and natural recovery; and S2, dynamic data synchronous acquisition.In the application, all active barometric pressure control and vibration generating devices are stopped, and the barometric pressure is naturally recovered, so that the pollution of low-pressure box vibration to sensor calibration data is fundamentally eliminated, the purity of the calibration data is ensured, the physical relationship between the barometric pressure and the sensor output is truly reflected by the obtained barometric pressure drift correction coefficient, and the authenticity and accuracy of the calibration coefficient are significantly improved; in view of the non-natural problem in the calibration process of the prior art, the barometric pressure natural recovery process is closer to the barometric pressure change scene experienced by the sensor in actual outdoor application, so that the generalization ability and compensation effect of the calibrated barometric pressure coefficient in the actual environment are better, and the adaptability problem that the accuracy required in the laboratory is high and the error in the field is large is effectively solved.
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