改进的MEMS陀螺随机噪声自适应Kalman实时滤波方法

Modified Adaptive Real-time Filtering Algorithm for MEMS Gyroscope Random Noise

针对微机电系统陀螺易受环境影响和稳定性能较差,导致建立的随机漂移模型参数和随机噪声统计特性变化的问题,提出了一种改进的指数渐消记忆自适应Kalman实时滤波方法.通过分析实测数据确定随机漂移自回归滑动平均模型阶数,在此基础上利用递推最小二乘法对模型参数进行实时更新.根据陀螺噪声参数特点,提出了基于渐消记忆因子的Allan方差分析法和Sage-Husa自适应滤波算法同时对Q和R进行参数估计的实时滤波方法,避免了系统状态估计和量测噪声参数估计的相互耦合和制约.实验结果表明:相比标准Kalman滤波补偿方法及传统的固定Q阵只对R阵做自适应估计的滤波方法,本文方法能够更加有效地对MEMS陀螺随机漂移误差进行实时补偿,具有较好的适应性和稳定性.

A modified Kalman real-time adaptive filter method was proposed for micro-electro-mechanical system gyro which is easy to be susceptible to environmental influences and stability,and lead to the problems that the established random drift model parameter and the noise statistical property change.The order of random drift ARMA model was determined by analyzing the measured data.On this basis,the recursive least square method was used to update the model parameters in real time.According to the characteristics of gyro noise parameters,a real-time filtering method of Allan variance analysis based on fading memory factor and Sage-Husa adaptive filter algorithm was proposed to estimate the parameters of Q and R at the same time.The coupling and restriction of system state estimation and measurement noise parameter estimation were avoided.The experimental results show that,compared with the standard Kalman filter compensation method,the proposed method in this paper can compensate the random drift error of MEMS gyro more effectively in real time,and has better adaptability and stability.