Multi-layer model aided inertial navigation system for unmanned ground vehicles

Abstract

The inertial navigation system (INS) has been commonly adopted by unmanned ground vehicles (UGV), but it needs other systems to correct the error divergence, such as the Global Navigation Satellite System (GNSS). However, GNSS failures are inevitable, and INS must navigate independently in such situations, that the navigation errors will diverge fast. To solve this problem, the vehicle model aided INS is proposed. In this paper, three commonly used vehicle models are analyzed to conclude their disadvantages first, that contains vehicle kinematics model (VKM), the non-holonomic constraint (NHC) of VKM, and the vehicle dynamics model (VDM). Against their disadvantages, the multi-layer vehicle model aided INS is proposed. The proposed method divides INS error parameters into the sensor layer, system layer I, and system layer II. Then, the navigation information from INS is fused with the developed VKM and VDM at the system layer and the sensor layer respectively. Besides, the adaptive Kalman filter is designed based on the VKM error model, that the estimations can be protected from the observation errors when the VKM accuracy declines. Compared to the traditional vehicle model aided INS, the proposed method can improve the accuracy and robustness of the navigation system with acceptable computational complexity.