Sensor dynamic compensation method based on GAN and its application in shockwave measurement

Abstract

Limited by the dynamic characteristics of the sensor, the high-frequency signal will be distorted by the dynamic error after passing through the sensor, which will affect the accuracy of the real value. To reduce the dynamic error, it is necessary to obtain a high-precision dynamic compensation model. This paper provides a solution of compensation model based on the deep learning method. First, the problem of limited sensor dynamic data is solved by data augmentation through Deep Convolutional Generative Adversarial Network. After that, the sensor compensation model is obtained by Speech Enhancement Generative Adversarial Network and applied to step signals and shockwave signals. This compensation method can compensate a variety of sensors used in the dynamic measurement. It is verified by the pressure sensor as an example in this paper, the results are better than that of traditional ones, the overshoot can be reduced from 119.2% to 2.5%, and the rising time is 5.5μs. The innovation of this paper is that we find a way to use deep learning methods to compensate sensor dynamic error based on a small dataset. At the same time, it is proved that this method has strong versatility, which is not available in traditional sensor compensation methods. It also provides a feasible scheme for the application of deep learning in dynamic compensation model calculation.