BPNN辅助KF的MEMS陀螺仪数据处理方法

针对微机电系统(MEMS)陀螺仪数据误差建模不精确或无法给出模型的情况，提出了误差反馈（BP）神经网络辅助卡尔曼滤波对陀螺仪数据进行降噪处理的方法。分析卡尔曼滤波器的系统噪声方差Q矩阵可知，当模型不精确时可通过Q补偿。基于BP神经网络优化Q值原理，首先把采集到的MEMS陀螺仪数据输入卡尔曼滤波器得到Q；再把新息、滤波增益、量测噪声方差输入神经网络，把Q作为神经网络的输出，神经网络优化系统噪声协方差矩阵得到Q*；最后将Q*作为卡尔曼滤波算法系统噪声方差矩阵。实验结果表明，在建模不精确的情况下该方法也能有效提高陀螺仪的精度。

Research on Data Processing Method of MEMS Gyroscope Based on BPNN Assisted Kalman Filter

Aiming at the inaccurate data modeling of MEMS gyroscope or the inability to give a model, a method to reduce the noise of gyroscope data by BP neural network (BPNN) assisted Kalman filtering is proposed in this paper. Analysis of the systematic noise variance Q of the Kalman filter shows that when the model is not accurate, it can be compensated by Q. Based on the principle of BP neural network to optimize Q value, the acquired MEMS gyroscope data were input into the Kalman filter to obtain Q firstly. Then the innovation, filter gain and measurement noise variance are input into the neural network, and Q is used as the output of the neural network. The system noise covariance matrix is optimized by the neural network to obtain Q*. And finally Q* is used as the noise variance matrix of the Kalman filter system. The experimental results show that the method can effectively improve the accuracy of the gyroscope in the case of inaccurate modeling.