基于分布式Kalman滤波和BP神经网络组合的静电陀螺漂移辨识方法

提出一种新的方法,把分布式Kalman滤波(DKF)方法与后向传播神经网络(BPNN)技术相结合,用于静电陀螺漂移的模型辨识.首先,为了消除测量噪声影响,将同一个静电陀螺带有噪声的多次测量数据集映射到一个虚拟的传感器网络中,然后采用具有嵌入式紧致滤波功能的DKF对映射数据进行滤波预处理.在此基础上,将预处理结果转换为用于训练神经网络的输入数据和输出数据,然后采用BPNN辨识静电陀螺漂移.实验表明,该方法可有效用于陀螺漂移的模型辨识.

COMBINATION OF DISTRIBUTED KALMAN FILTER AND BP NEURAL NETWORK FOR ESG BIAS MODEL IDENTIFICATION

By combining the distributed Kalman filter (DKF) with the back propagation neural network (BPNN),a novel method is proposed to identify the bias of electrostatic suspended gyroscope (ESG).Firstly,the data sets of into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets.After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias.As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.