Temperature drift compensation of a FOG based on an HKSVM optimized by an improved hybrid BAS-GSA algorithm

Drift calibration method of Fabry-Perot filters using two-stage decomposition and hybrid modeling

Although tunable Fabry-Perot (F-P) filters are widely acknowledged as fiber Bragg grating (FBG) demodulators, F-P filters exhibit drift error when subjected to ambient temperature and piezo-electrical transducer (PZT) hysteresis. To address the drift issue, the majority of the existing literature makes use of additional devices like the F-P etalon and gas chamber. In this study, a novel drift calibration method based on two-stage decomposition and hybrid modeling is proposed. The initial drift error sequences are broken down into three frequency components using the variational mode decomposition (VMD), and the medium-frequency components are further broken down using the secondary VMD. The initial drift error sequences are significantly simplified by the two-stage VMD. On this foundation, the long short-term memory (LSTM) network and polynomial fitting (PF) are used to forecast the low-frequency and high-frequency drift errors, respectively. The LSTM enables the prediction of intricate nonlinear local behaviors, while the PF method predicts the overall trend. The benefits of LSTM and PF can be effectively utilized in this manner. Compared to the single-stage decomposition, two-stage decomposition achieves superior results. The suggested method is an affordable and effective alternative to the current drift calibration techniques.

Long short-term memory network of machine learning for compensating temperature error of a fiber optic gyroscope independent of the temperature sensor

We present an artificial intelligence compensation method for temperature error of a fiber optic gyroscope (FOG). The difference from the existing methods is that the compensation model finally determined by this method only uses the FOG's data to complete the regression prediction of the temperature error and eliminate the dependency on the temperature sensor. In the experimental stage, the proposed method performs temperature experiments with three varying trends of temperature heating, holding, and cooling and obtains sufficient output data sets of the FOG. Taking the output time series of the FOG as the input sample and based on the long short-term memory network of machine learning, the training, validation, and test of the model are completed. From the two perspectives of network learning ability and the improvement degree of the FOG's performance, four indicators, including root mean square error, error cumulative distribution function, FOG bias stability, and Allan variance analysis are selected to evaluate the performance of the compensation model comprehensively. Compared with the existing methods using temperature information for prediction and compensation, the results show that the error compensation method without temperature information proposed can effectively improve the accuracy of the FOG and reduce the complexity of the compensation system. The work can also provide technical references for error compensation of other sensors.