GNSS失锁下基于混合预测模型的POS误差估计方法

POS error estimation method based on hybrid prediction model during GNSS outages

针对GNSS信号受遮挡或干扰导致的位姿测量系统(POS)量测信息失锁问题,提出一种基于多元线性回归(MLR)和径向基函数神经网络(RBFNN)的混合预测方法,用于GNSS失锁期间的POS导航误差预测。该方法利用Hodrick-Prescott(HP)滤波将POS导航误差样本数据分解成趋势性成分序列与波动性成分序列,分别采用MLR和RBFNN对其进行预测建模,充分表征POS导航误差数据的线性与非线性特征。车载实验结果表明,所提出的基于MLR/RBFNN混合预测方法与标准Kalman滤波方法相比,位置误差精度提高72.9%~89.1%,速度误差精度提高50.1%~60.8%,其位置和速度误差统计结果均优于单一的MLR预测模型和RBFNN预测模型。

Aiming at POS observation loss caused by GNSS signal occlusion and interference,a hybrid prediction method based on multiple linear regression(MLR)and radial basis function neural network(RBFNN)is proposed for the prediction of POS errors during GNSS outages.Hodrick–Prescott(HP)filter is utilized in the proposed method to decompose the POS errors sample data into the trend and fluctuation series,which are separately intended for use with MLR and RBFNN modeling in order to fully characterize both the linearity and nonlinearity of POS errors.The results of vehicle test show that the percentage improvement of the proposed MLR/RBFNN hybrid prediction method in position and velocity accuracy is found to improve by 72.9%~89.1%and 50.1%~60.8%compared with the standard Kalman filter,which is superior remarkably to the single MLR and RBFNN.