Calibration of Multi-dimensional Air Pressure Sensor Based on LSTM

The calibration of the air pressure sensor is of great significance for improving the measurement accuracy of the sensor and the accuracy of atmospheric prediction. In view of the problem that there are few deep learning methods that can be applied to sensor calibration, and the accuracy cannot meet the requirements of practical applications, this paper considers the temporal characteristics of the measurement data of the air pressure sensor, and proposes a multi-dimensional air pressure sensor calibration based on LSTM. The test results on the pressure sensor data set in the interval of [0 kPa, 1100 kPa] and [−30 ℃, 30 ℃] show that the error of the pressure sensor is reduced from 1.4 kPa to about 0.55 kPa compared with other sensor calibration methods proposed in this paper. In addition to better calibration results, it has good generalization ability, which can be applied to similar sensor calibration.