稳态卡尔曼滤波优化方法研究
Research on optimization method of steady-state Kalman filter
李珂 1赵建涛 1胡玉龙 1唐艺菁1
作者信息
- 1. 西安微电子技术研究所,陕西 西安 710054
- 折叠
摘要
微机电系统(Micro-electromechanical Systems,MEMS)陀螺仪测量精度低、噪声大等不足,亟需对微机电陀螺仪输出信号进行数字滤波处理.针对传统卡尔曼滤波方法在信号处理初期对协方差初始值依赖较大的问题,基于陀螺仪实时输出信号,在线分析数据、辨识误差类型,利用动态性高的时间序列模型描述陀螺仪输出角速度,实时构建陀螺仪信号自回归一阶模型,根据自回归一阶模型分析卡尔曼滤波器协方差、状态预测误差、量测误差等,深入研究稳态卡尔曼滤波实时优化方法,直接计算每一个时刻最优方差和最优卡尔曼增益,使得每一个时刻都可以达到稳态情况下的最优估计,消除传统卡尔曼滤波方法初期对协方差初始值的依赖.采用MPU6050 作为微机电陀螺仪测试样机进行了试验验证,结果表明:与传统卡尔曼滤波方法相比,稳态卡尔曼滤波方法在滤波初期输出结果的平均值可提升 93.33%,标准差可提升 96.87%,实现微机电陀螺仪信号最优估计,满足微机电陀螺仪实时滤波、快速优化的需求.
Abstract
The measurement accuracy of MEMS(Micro-ElectroMechanical Systems)gyroscope is low and the noise is high,so it is urgent to process the output signal of MEMS gyroscope digitally.Aiming at the problem that the traditional Kalman filtering method relies heavily on the initial value of the covariance at the initial stage of signal processing,based on the real-time output signal of gyroscope,the data is analyzed online,error types are identified,and the output angular velocity of gyroscope is described by using the dynamic time series model,and the autoregressive first-order model of gyroscope signal is constructed in real time.Based on the autoregressive first-order model,the covariance,state prediction error and measurement error of Kalman filter are analyzed,and the real-time optimization method of steady-state Kalman filter is deeply studied,and the optimal variance and optimal Kalman gain at every moment are directly calculated,so that the optimal estimation at every moment can be achieved in steady-state condition.The dependence of traditional Kalman filter on the initial value of covariance is eliminated.Using MPU6050 as MEMS gyroscope test prototype,the test results show that compared with the traditional Kalman filtering method,the steady-state Kalman filtering method can improve the average value of the output result by 93.33%and the standard difference by 96.87%in the initial filtering stage,realizing the optimal estimation of MEMS gyroscope signal and meeting the requirements of real-time filtering and rapid optimization of MEMS gyroscope.
关键词
微机电陀螺仪/实时优化/时间序列模型/稳态卡尔曼滤波Key words
MEMS gyroscope/real time optimization/time series model/steady-state Kalman filtering引用本文复制引用
出版年
2024
NETL
NSTL
万方数据