硅微陀螺阵列信号处理技术研究

Research on Signal Procession of Silicon Micro-gyroscope Array

基于一种新的硅微陀螺随机漂移模型和被测角速度的随机游走模型,建立硅微陀螺阵列随机漂移过程的Kalman滤波方程。通过数据融合方法,在选择合适的陀螺间互相关系数的基础上,可以将硅微陀螺的静态漂移从52.1度/小时降低为0.465度/小时。利用同类多传感器的数据融合和被测量动态信号的差分技术,可以实时辨识出硅微陀螺的随机漂移,通过已被检测出的动态信号的进一步Kalman滤波,最终可将被测信号的信噪比提高11dB。

A new random drift model and the measured rate random walk model of the silicon micro - gyroscope are given. Based on the model, Kalman filtering equations have been built. By the data fusion, the static drift of the gyroscope array can be decreased from 52. ldeg/hr to 0.465deg/hr if the correlative coefficient between the individual gyroscopes is selected properly. The data fusion and the signal difference from the homogeneous multi-sensors can identify the real-time drift from the individual gyroscope dynamic measurement. If the tested out dynamic signal is Kalman-filtered further more, the signal noise rate of the tested dynamic signal can be improved l1dB.