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

The inertial navigation system (INS) has been commonly adopted for unmanned ground vehicles, but it needs other systems to correct for error divergence, such as the Global Navigation Satellite System (GNSS). However, GNSS failures are inevitable, and INS must navigate independently in such situations, meaning that 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, first to discuss their disadvantages, that they contain the vehicle kinematics model (VKM), the non-holonomic constraint of VKM, and the vehicle dynamics model (VDM). Against their disadvantages, the multi-layer vehicle model aided INS is proposed. The proposed method divides the INS error parameters into the sensor layer, system layer I, and system layer II. Then, the navigation information from the INS is fused with our developed VKM and VDM at the system layer and the sensor layer respectively. Additionaly, the design of the adaptive Kalman filter is based on the VKM error model, such that the estimations can be protected from 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.