GNSS/INS/OD/NHC Adaptive Integrated Navigation Method Considering the Vehicle Motion State

Abstract

Global navigation satellite systems (GNSSs) integrated with inertial navigation systems (INSs) have been widely applied in many intelligent transport systems. At present, integrating GNSS, microelectromechanical system (MEMS), on- board controller area network (CAN) sensors, and vehicle motion constraint information is the most practical and low-cost vehicle multifusion navigation scheme when GNSS outages. It is especially true when using 3-D velocity from nonholonomic constraints (NHC) and odometer (OD). The GNSS/INS/OD/NHC integration, however, has the problem of inaccuracy in lateral velocity constraint parameters. To overcome this problem, a back propagation (BP) neural network-based GNSS/INS/OD/NHC adaptive integrated navigation method considering the vehicle motion state is proposed in this article. The relationship between the forward velocity, the heading angular velocity, and the lateral velocity is analyzed and considered when the NHC lateral velocity constraint modeling is established by using the BP neural network. To assess the performance of this method, three sets of real land vehicle data are tested with intentional GNSS signal interruption at different vehicle states. The performances of the classic INS/NHC model, INS/OD/NHC integration, and the proposed method are compared, respectively. Experimental results show that the mean error and RMSE of the lateral velocity predicted by the proposed method is 0.007 and 0.049 m/s. The mean 3-D RMSE of the positioning errors and the velocity errors of the proposed method is 1.515 m and 0.182 m/s respectively, which are improved by 54.86% and 44.85% compared with that of the classic INS/OD/NHC integration.