State transformation extended Kalman filter for UAV/UGV cross-domain cooperative navigation

In the high-rise dense urban environment, unmanned ground vehicles (UGV) cannot receive useful GNSS information when working in dense streets, and can only rely on other sensors for navigation, with poor accuracy. Aiming at the problem, a cross-domain cooperative navigation method of UAV /UGV based on state transformation extended Kalman filter (ST-EKF) is proposed. The unmanned aerial vehicle (UAV) nodes in the cooperative formation fly in the area where GNSS signals can be received over the building. The UGV nodes work in the streets without GNSS signals, and the navigation information between the nodes can be exchanged through relative measurement sensors and data links. In order to further improve the accuracy of cooperative navigation, the navigation information is fused by using the method based on ST-EKF, and the navigation error of each unmanned vehicle is estimated and corrected. The simulation results show that the maximum position error of the main unmanned vehicle is less than 3 m, and the root mean square error of the horizontal position is reduced by 80.62% compared with the traditional method. The maximum position error of the slave vehicle is less than 5 m, and the root mean square error of the horizontal position of the two slave vehicles is reduced by 74.24% and 77.99% respectively. © 2023 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.