基于ARMA建模的MEMS陀螺随机误差补偿改进算法研究

基于MEMS技术生产的MEMS器件,具有体积小、重量轻、成本低、耐冲击性、高可靠性等特点,它被广泛应用于动态水平测量装置当中。但是由于外界环境的干扰,MEMS器件的测量精度一直难以达到实际应用水平。分析了在动态水平测量当中影响MEMS器件测量精度的各种因素之后,提出了一种基于ARMA模型的针对MEMS陀螺器件随机误差补偿的改进型算法。以某型号的陀螺的随机误差为研究对象进行实验验证,结果表明,MEMS陀螺的测量精度在滤波之后有了明显的提高。经过改进后的卡尔曼滤波器和引入自适应渐消因子的卡尔曼滤波器,在静态环境下,它们的误差标准差分别降为原始误差的3.75%和4.8%,动态环境下的滤波精度也得到有效提高。证明该方法是可行的和有效的,具有较大的工程实践意义。

An improved algorithm for random error compensation of MEMS gyroscopes based on ARMA model

With the advantages of small volume, light weight, low cost, shock resistance, high reliability, MEMS devices in dynamic hor But due to the based on MEMS technology are applied izontal angle measuring devices widely interference of external environments the measuring accuracy of MEMS devices is difficult to achieve the level of practical application . The factors influencing the measuring accuracy of MEMS devices in the measurement are analyzed. Aiming at the random error compensation of MEMS gyros, an improved algorithm is proposed based on ARMA model. The tests are done with certain type of gyro as example. The test results show that the measuring accuracy of MEMS gyroscopes is improved significantly after filtering. The error of Kalman filters improved or introduced the adaptive fading factor, are reduced to 3.75% and 4.8% of the original error under the static condition. The filtering precision is also improved effectively in dynamic environments. It is proved that the method is feasible and effective and of great practical significance.