# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 5 Conclusions  
Affected by external vibration, temperature change, and other factors, fiber-optic gyro in a photoelectric system usually causes complex errors, which can seriously reduce servo performance and even cause divergence of the whole system. Therefore, gyro error compensation techniques have attracted the attention of many experts for a long time. However, according to the current literature,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Noise Type** & **QN** & **ARW** & **ZOI** & **RRW** & **RR** \\ \hline Original signal & 28.4648 & \(-\)23.8923 & 6.7926 & \(-\)0.4592 & 0.0089 \\ ARIMA & 22.0082 & \(-\)21.9880 & 6.5200 & \(-\)0.4321 & 0.0086 \\ ELMAN & 20.1009 & \(-\)21.1266 & 6.4341 & \(-\)0.4294 & 0.0081 \\ ARIMA + ELMAN & \(-\)0.1692 & 0.0382 & 0.0323 & \(-\)0.0042 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of coefficients before and after compensation. Here, QN indicates Quantization Noise, ARW indicates Angle Random Walk, ZOI indicates Zero Offset Instability, RRW indicates Rate Random Walk, and RR indicates Rate Ramp.  
Figure 12: Compensation results based on the ARIMA-Elman method.  
due to the non-stationary randomness, the typically-used either single linear approximation model or single nonlinear neural network approximation model did not work well. In this paper, based on the lift-wavelet separation and reconstruction technique, the relative low-frequency drift error component was first obtained. Then, in order to weaken the randomness of the data sequence, an ashing technique was operated and drift error separated into low-volatility and high-volatility components. After that, the ARIMA model and recurrent Elman model was applied, to compensate for each component independently. As the hybrid ARIMA-Elman model combines the advantages of both of the two models, it can fully collect complex nonlinear characteristics hidden in non-stationary drift error data and approximate them better, theoretically. Furthermore, taking into consideration that the commonly used least-squares method, genetic optimization algorithms, and so on, easily fall into local extreme values and model parameters are difficult to determine, in this paper an intelligent difference algorithm was proposed to identify parameters of the hybrid model. The algorithm had few parameters to adjust, which saved much time during the training stage. The gyro data was actually collected and processed, and, based on the famous Allan variance, the data (before and after compensation) was compared and analyzed, respectively. Experimental results showed that the proposed method in the study can filter out the non-stationary noise in the gyro quickly and effectively. Furthermore, to test the generalization ability of the method, another set of data was collected and compensated using the proposed hybrid model and the above-mentioned data process technique. The experimental results meanwhile showed that the proposed method in this paper has good generalization capability, and can meet actual engineering needs satisfactorily.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
The paper was supported by the National Science Foundation of China (grant no. 61705052) and the Fundamental Research Funds for the Central Universities (grant no. HIT.NSRIF.201630).  
The authors declare no conflict of interest.