The vertical accuracy improvement method considering gravitational anomaly for SINS/GNSS

Abstract

The vertical information (vertical velocity and altitude) is important in three-dimensional (3D) navigation. In order to improve the vertical accuracy of Strapdown Inertial Navigation System/Global Navigation Satellite System integrated navigation (SINS/GNSS) while reducing costs, firstly, the propagation laws of vertical error sources in two types of SINS/GNSS (vertical velocity set to 0 and not set to 0) are systematically analysed. Furthermore, the vertical accuracy improvement method considering gravitational anomaly is proposed. In the method, gravitational anomaly is considered as one of the vertical error sources. Then, the processing method of error sources in integrated navigation is referenced, and two processing modes of gravitational anomaly are designed. The first way is to represent gravitational anomaly as one of the system states of SINS/GNSS. The number of dimensions for the system is expanded from 15 to 16. The corresponding mathematical model is derived to "absorb" the vertical errors caused by gravitational anomaly. The second is to represent the gravitational anomaly as one of the vertical system noises. Thereby, Kalman filter is adjusted in real time by the above adaptive way to improve the accuracy of the estimated state. Then, the corresponding errors are suppressed. The field experiments show that the both modes of the proposed method can effectively improve the vertical accuracy.

Integration of two different road network models for emergency rescue pathfinding in indoor and outdoor environments

“…The topological network model consists of two elements: 'Node' and 'Edge', as described above. The element 'Node' represents the mapping of building elements, while the element 'Edge' inserted between adjacent nodes represents the spatial topological relationships [10,22]. The mainstream methods for generating a topological road network model include the medial axis transformation (MAT), the generalized Voronoi graphs (GVGs) [23,24], etc.…”