Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks
Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks
复制标题
基于体积卡尔曼滤波器和神经网络的 GPS 中断期间 MEMS-INS/GPS 系统的双重优化
DOI：
10.1016/j.ymssp.2019.07.003
复制
发表时间：
2019-11-01
影响因子：
8.4
通讯作者：
Liu, Jun
中科院分区：
文献类型：
--
作者：
Shen, Chong;Zhang, Yu;Liu, Jun
为了提高基于微机电系统（MEMS）的惯性导航系统（INS）/全球定位系统（GPS）组合导航系统的性能，提出了一种基于容积卡尔曼滤波（CKF）-多层感知器（MLP）和径向基函数（RBF）-CKF的双重优化方案，用于GPS故障时的位置和速度误差补偿。该方法具有以下优点：（1）CKF-MLP的泛化能力远优于扩展卡尔曼滤波（EKF）-MLP和无迹卡尔曼滤波（UKF）-MLP，即使在GPS长时间中断时，CKF-MLP也能提供高精度的位置信息;（ii）RBF-CKF的误差估计的准确性高于其他神经网络辅助的CKF方法，例如Adaboost-CKF和随机森林（RF）-CKF，（3）在CKF中加入RBF，建立了RBF-CKF中滤波器参数与估计误差之间的关系;（iv）在GPS中断期间，在所提出的双重优化方案中预测和补偿位置误差和速度误差。收集现场测试数据以评估所提出的解决方案。实验结果表明：（i）采用所提出的CKF-MLP策略，东边位置的速度均方根误差减小了77%，为0.34 m/s;（ii）采用所提出的RBF-CKF策略，北方位置的速度均方根误差即使在500 s的长停机时间内也保持在23.11 m，并且RBF-CKF策略有效地抑制了其他方法的发散;（iii）使用不同估计器的双重优化过程提供了比单一优化方法更好的误差补偿结果，这表明所提出的解决方案导致基于MEMS的INS/GPS导航系统的更好性能。(C)2019爱思唯尔有限公司版权所有。
To improve the performance of a microelectromechanical-system (MEMS)-based inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, a dual optimization scheme comprising a cubature Kalman filter (CKF)-multiple layer perceptron (MLP) and radial basis function (RBF)-CKF is proposed for the compensation of position and velocity errors during GPS outages. The proposed method has advantages: (i) The generalization ability of the CKF-MLP is much better than that of other methods, such as the extended Kalman filter (EKF)-MLP and unscented Kalman filter (UKF)-MLP, and the proposed CKF-MLP provides highly accurate position information even during long GPS outages; (ii) The accuracy of the error estimation of the RBF-CKF is higher than that of other neural-network-assisted CKF methods, such as the Adaboost-CKF and random forest (RF)-CKF, and the proposed CKF-MLP can estimate the weights of the MLP adaptively and achieve an appropriate internal structure when the GPS signal is available; (iii) The RBF is added to the CKF to establish the relationship between filter parameters and estimation error in the proposed RBF-CKF; (iv) During a GPS outage, position errors and velocity errors are predicted and compensated for in the proposed dual optimization scheme. Field test data are collected to evaluate the proposed solution. Experimental results show that (i) the root-mean-square error of the velocity for an eastern position is reduced by 77% to 0.34 m/s using the proposed CKF-MLP strategy; (ii) the root-mean-square error of a northern position determined by the proposed RBF-CKF remains at 23.11 m even during long outages of 500 s, and the RBF-CKF effectively suppresses the divergence seen for other methods; and (iii) the dual optimization process using different estimators provides better error compensation results than a single optimization method, which demonstrates that the proposed solution leads to the better performance of a MEMS-based INS/GPS navigation system. (C) 2019 Elsevier Ltd. All rights reserved.