基于振动陀螺的动态零位校准及其误差补偿

根据固态振动陀螺输出信号和噪声特点对其构成的低成本惯性测量单元(IMU)的原始传感信号进行了快速小波滤波和灰色理论建模处理。运用累加生成操作(AGO)方法得到有规则的单值对应非线性函数,并获得陀螺零位输出在三维空间中的单值映射模型。以时间和温度为输入,根据灰色神经网络建立陀螺的漂移模型,对累加生成方法生成的单值对应非线性函数进行逼近,从而提高了动态测量精度。同时采用活动阈值融合算法,优化陀螺和加速度计动态测量数据。实验证明,上述方法和算法有效提高了系统测量精度。

Dynamic Zero Output Adjustment and System Error Compensation Based on Low Cost Vibration Gyro

The sensor signal filtered through fast wavelet and compensated by Grey model was proposed according to the characteristics of signal and noise output.Grey accumulate operation(AGO) was adopted to achieve the regularly single-mapping function and the model of single-mapping of gyro's zero output in three dimension coordinates.The model based on the Grey neural network approximated the function of created by the AGO algorithm and improved the measurement accuracy of IMU.At the same time,the active threshold based on the fusion algorithm was used to optimize the measurement datum of gyro and accelerometer.Experiments proved these method and algorithm increased the system's accuracy successfully.