Performance Analysis of GNSS/MIMU Tight Fusion Positioning Model with Complex Scene Feature Constraints

Abstract

In order to meet the requirements of high-precision vehicle positioning in complex scenes, an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed, non-integrity, attitude, and odometer constraint models. In this model, the robust equivalent gain matrix is constructed by the IGG-Ⅲ method to weaken the influence of gross error, and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment, so as to improve the solution performance of filtering system and realize high-precision position, attitude and velocity measurement when GNSS signal is unlocked. A real test on a road over 600km demonstrates that, in about 100km shaded environment, the fixed rate of GNSS ambiguity resolution in the shaded road is 10% higher than that of GNSS only ambiguity resolution. For all the test, the positioning accuracy can reach the centimeter level in an open environment, better than 0.6m in the tree shaded environment, better than 1.5m in the three-dimensional traffic environment, and can still maintain a positioning accuracy of 0.1m within 10s when the satellite is unlocked in the tunnel scene. The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints, which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.