High-Accuracy Autonomous Navigation Algorithm For Long-Distance Land Navigation In GNSS-Denied Environments

The strapdown inertial navigation system (SINS), global navigation satellite system (GNSS) and odometer (OD) integrated navigation is one of the most common and effective navigation methods for land vehicles. However, in the complex environment where GNSS signals are degraded, such as urban tunnels and forest canyons, the accuracy of this algorithm is largely deteriorated. In order to improve the self-contained navigation ability of land vehicles in GNSS-Denied environments, this study uses the state transformation Kalman filter (ST-EKF) with better filtering robustness to replace the traditional extended Kalman filter (EKF) as the information fusion framework, and proposes a high-accuracy integrated navigation technology based on data fusion of SINS, OD, laser Doppler velocimeter (LDV) and Barometric altimeter (Baro). Simulation by post-processing two groups of the long distance fiber optic gyroscope inertial measurement unit (FOGIMU) /OD/LDV/Baro experimental data shows that the algorithm has high positioning accuracy in different driving environments. Meanwhile, compared with the integrated navigation algorithm of SINS/OD and SINS/LDV based on EKF, the horizontal and height positioning accuracy are greatly improved all.