Silicon Micro-machined Gyroscope Random Error Modeling And Compensation

With dramatically reduced cost, size, and weight, MEMS gyroscopes potentially earn a wide application spectrum in the aerospace industry, military, automotive and consumer electronics markets. Due to the imperfection of fabrication, the performances of MEMS gyroscopes are significantly limited. One of the methods to increase its available accuracy is error analysis, modeling and compensation.Firstly, the operative principles of the Silicon Micromachined Gyroscope (SMG) are studied.Secondly, the random error characteristics of SMG based on Allan variance are analyzed. The basic theory of Allan variance is studied comparing with the traditional standard variance. The influence of the sampling frequency on Allan variance is researched, and the reasonable sampling frequency is given. Allan variance method is used to characterize various types of noise term in the SMG data.The characteristic results of SMGs show that the main noise sources are the angle random walk (ARW) and bias instability (BI).Thirdly, the method of the suppression of SMG random errors are developed based on the ARMA model for Kalman filtering. According to the criteria of FPE and AIC, the model of SMG drift should be elected as ARMA(2,1) model. The algorithms and the applications of Kalman filtering based on time-series model are achieved.Fourthly, the real-time filtering system based on LPC1768 for SMGs is designed and realized. The real-time filtering system and control circuit of SMGs are assembled, and the performances of SMGs are tested. The results indicate that the scale factor is not attenuated after filtering. The bias instability is improved by 60%. Meanwhile, the result shows that improving the performance of the traditional Kalman filtering is achieved by reducing signal bandwidth.Fifthly, in order to solve the problem between the bandwidth and performance of filter, the adaptive filtering technology is researched. The application of adaptive line enhancement (ALE) is realized. Using the real-time filtering system based on adaptive line enhancement, the SMG are tested, and the testing data are analyzed the by Allan variance. The analysis result shows that quantization noise, angle random walk, bias instability is reduced by 40%,52%, 20%, respectively.At last, the SMG performances of the real-time filtering system based on adaptive line enhancement are tested. The results show that measurement range is±400°/s, scale factor is 19.3mv/°/s, nonlinearity is 815.9ppm, bias instability is 2.7°/h, and bias repeatability is 21.5°/h.