An innovation-based cycle-slip, multipath estimation, detection and mitigation method for tightly coupled GNSS/INS/Vision navigation in urban areas

TL;DR

An innovation-based cycle slip/multipath estimation, detection, and mitigation (I-EDM) method to reduce the influence of multipath effects and cycle slips on location induced by obstruction in urban settings is offered.

Abstract

Precise, consistent, and reliable positioning is crucial for a multitude of uses. In order to achieve high precision global positioning services, multi-sensor fusion techniques, such as the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/Vision integration system, combine the strengths of various sensors. This technique is essential for localization in complex environments and has been widely used in the mass market. However, frequent signal deterioration and blocking in urban environments exacerbates the degradation of GNSS positioning and negatively impacts the performance of the multi-sensor integration system. For GNSS pseudorange and carrier phase observation data in the urban environment, we offer an innovation-based cycle slip/multipath estimation, detection, and mitigation (I-EDM) method to reduce the influence of multipath effects and cycle slips on location induced by obstruction in urban settings. The method obtains the innovations of GNSS observations with the cluster analysis method. Then the innovations are used to detect the cycle slips and multipath. Compared with the residual-based method, the innovation-based method avoids the residual overfitting caused by the least square method, resulting in better detection of outliers within the GNSS observations. The vehicle tests carried out in urban settings verify the proposed approach. Experimental results indicate that the accuracy of 0.23m, 0.11m, and 0.31m in the east, north and up components can be achieved by the GNSS/INS/Vision tightly coupled system with the I-EDM method, which has a maximum of 21.6% improvement when compared with the residual-based EDM (R-EDM) method.

Figures (13)

• Figure 1: Implementation of RTK/MEMS/Vision tightly coupled system
• Figure 2: The augmentation of the optimal value and covariance when RTK observation is recorded. The optimal value and covariance from the newly added and that copied from the previous moment together constitute the optimal value and covariance of the current moment.
• Figure 3: Implementation of pseudorange processing in I-EDM algorithm. The algorithm utilizes the innovation to detect the multipath in the observations, thereby avoiding the estimation of the parameters with least square method.
• Figure 4: Implementation of carrier phase processing in I-EDM algorithm. The algorithm detects cycle slips and models multipath with between-station between-epoch carrier phase observations.
• Figure 5: The hardware equipment used for data collection. The GNSS/INS/Vision experimental data is collected with a stereo camera, a MEMS IMU, a tactical IMU, and a GNSS antenna.