Robust state and protection-level estimation within tightly coupled GNSS/INS navigation system

In autonomous applications for mobility and transport, a high-rate and highly accurate vehicle-state estimation is achieved by fusing measurements of global navigation satellite systems (GNSS) and inertial sensors. The state estimation and its protection-level generation often suffer from satellite-signal disturbances in urban environments and subsequent poor parametrization of the satellite observables. Thus, we propose an innovative scheme involving an extended H-infinity filter (EHF) for robust state estimation and zonotope for the protection-level generation. This scheme is shown as part of a tightly coupled navigation system based on an inertial navigation system and aided by the GPS/Galileo dual-constellation satellite navigation system. Specifically, GNSS pseudorange and deltarange observables are utilized. The experimental results of post-processing a real-world dataset show significant advantages of EHF against a conventional extended Kalman filter regarding the navigation accuracy and robustness under various GNSS measurement parametrizations and environmental circumstances. The zonotope-based protection-level calculation is proven valid, computationally affordable, and feasible for real-time implementations.

Automated summary

To address the issues of satellite signal disturbances and poor parametrization in urban environments, an innovative scheme based on extended H-infinity filter and zonotope is proposed for high-rate and highly accurate vehicle-state estimation and protection-level generation. Experimental results demonstrate significant advantages of this scheme over the conventional extended Kalman filter in terms of navigation accuracy and robustness under various GNSS measurement parametrizations and environmental circumstances. The zonotope-based protection-level calculation is proven to be valid, computationally affordable, and feasible for real-time implementations.