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Mapping function (MF) converts the line-of-sight slant total electron content (STEC) into the vertical total electron content (VTEC), and vice versa. In an MF, an essential parameter is the ionospheric effective height. However, the inhomogeneous ionosphere makes this height vary spatially and temporally, meaning it is not a global constant. In the paper, we review several mapping functions and propose a mapping function that utilizes the ionospheric varying height (IVH). We investigate impacts of the IVH on mapping errors and on the ionospheric modeling, as well as on the satellite and receiver differential code biases (DCBs). Our analysis results indicate that the mapping errors using IVH are smaller than those from the fixed height of 450 km. The integral height achieves smaller mapping errors than using a fixed height of 450 km, an improvement of about 8% when compared with the fixed height of 450 km. And 35% smaller mapping errors were found using HmF2 at the lower latitude. Also, the effects of IVH on the satellite DCBs are about 0.1 ns, and larger impacts on the receiver DCBs at 1.0 ns.

Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) has drawn increasing interests in autonomous driving. However, LiDAR-SLAM suffers from accumulating errors which can be significantly mitigated by Global Navigation Satellite System (GNSS). Precise Point Positioning (PPP), an accurate GNSS operation mode independent of base stations, gains growing popularity in unmanned systems. Considering the features of the two technologies, LiDAR-SLAM and PPP, this paper proposes a SLAM system, namely P 3 -LOAM (PPP based LiDAR Odometry and Mapping) which couples LiDAR-SLAM and PPP. For better integration, we derive LiDAR-SLAM positioning covariance by using Singular Value Decomposition (SVD) Jacobian model, since SVD provides an explicit analytic solution of Iterative Closest Point (ICP), which is a key issue in LiDAR-SLAM. A novel method is then proposed to evaluate the estimated LiDAR-SLAM covariance.In addition, to increase the reliability of GNSS in urban canyon environment, we develop a LiDAR-SLAM assisted GNSS Receiver Autonomous Integrity Monitoring (RAIM) algorithm. Finally, we validate P 3 -LOAM with UrbanNav, a challenging public dataset in urban canyon environment. Comprehensive test results prove that, in terms of accuracy and availability, P 3 -LOAM outperforms benchmarks such as Single Point Positioning (SPP), PPP, LeGO-LOAM, SPP-LOAM, and the loosely coupled navigation system proposed by the publisher of UrbanNav.

Differential Code Biases (DCBs) are much more relevant when GNSS data processing with code measurements is involved, such as in ionospheric sensing, positioning, and timing. The current approach to estimate DCBs is based on carrier‐phase smoothed code observations together with ionospheric modeling. A limiting factor of the method is the effect of the leveling errors from the smoothing process on the DCB estimate. To reduce the leveling errors, a new DCB estimation method based on an Uncombined Precise Point Positioning (UPPP) model is proposed. A month's data from a global network in a high solar activity year from May 1 to 31, 2014 are processed to validate the method. The results show that most satellite DCB estimates are found to be more stable than when using the smoothed code method. The improvement can be up to about 0.22 ns. The stability and accuracy of the receiver DCB estimates is also enhanced. Copyright © 2017 Institute of Navigation

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