Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm.

Global Navigation Satellite Systems (GNSS) Carrier Phase (CP)-based high-precision positioning techniques have been widely used in geodesy, attitude determination, engineering survey and agricultural applications. With the modernisation of GNSS, multi-constellation and multi-frequency data processing is one of the foci of current GNSS research. The GNSS development authorities have better designs for the new signals, which are aimed for fast acquisition for civil users, less susceptible to interference and multipath, and having lower measurement noise. However, how good are the new signals in practice? The aim of this paper is to provide an early assessment of the newly available signals as well as assessment of the other currently available signals. The signal quality of the multi-GNSS (GPS, GLONASS, Galileo, BDS and QZSS) is assessed by looking at their zero-baseline Double Difference (DD) CP residuals. The impacts of multi-GNSS multi-frequency signals on single-epoch positioning are investigated in terms of accuracy, precision and fixed solution availability with known short baselines.

Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey.

As a frequently-occurred phenomenon in the high-latitude region, ionospheric scintillations affect the stable service of the positioning navigation and timing service of the Global Navigation Satellite System (GNSS), calling for an urgent need of monitoring the scintillations accurately. The monitoring of scintillations usually adopts a special type of receiver, called an ionospheric scintillation monitoring receiver (ISMR), which cannot cover the whole high-latitude region due to its loss distribution. Geodetic receivers are densely distributed, but set at a 30s-sampling-interval usually. It is a controversial issue, namely, the accuracy of the scintillation index extracted from 30s-sampling-interval observations. This paper evaluates the accuracy of two 30s-sampling-interval indices in monitoring scintillations from both the time and space aspects using observations collected in the whole year of 2020. The accuracy in the time aspect is assessed with the phase scintillation index from ISMR as the reference through the following three-pronged approaches, i.e., the accuracy of the daily scintillation occurrence rates in the year 2020, the correlation with space weather parameters, and the variation pattern of the scintillation occurrence rate with the local time and day of the year 2020. The accuracy in space is studied based on the scintillation grid model considering the following two aspects, i.e., the scintillation monitoring performance in a Swarm satellite observation arc, and the statistical scintillation occurrence rate in the whole research region throughout the year 2020. The results of this paper reveal the efficiency of the 30s-sampling-interval scintillation indices in monitoring scintillations and detecting the occurrence patterns in the high-latitude region. The outcome of this paper can provide a basic idea for introducing the widely distributed geodetic receivers to monitor and model the scintillations in the high-latitude region.