A new machine learning based method for multi-GNSS data quality assurance and multipath detection

Global Navigation Satellite Systems (GNSS) based high-precision positioning techniques have been widely used in geodesy, attitude determination, engineering survey and agricultural applications. With the modernisation of GNSS, the number of visible satellites and new signals are increasing. Multi-constellation and multi-frequency data provide users with more observations, and hence increase redundancy in parameter estimation. However, increased number of satellites may increase the chance of multipath errors, especially in difficult environments. Therefore, this thesis aims at characterising the measurement signal quality of all available and new signals of multi-GNSS (GPS, GLONASS, Galileo, BDS, and QZSS) with real data. Also, a new multipath detection model based on machine learning methods is developed. The measurement noise levels in all currently available signals have been studied by investigating their double difference (DD) carrier phase residuals. The positioning precision, accuracy, and ambiguity success rate have been assessed using the selected individual GNSS constellations and their selected combinations with static and kinematic antennas in low multipath and severe multipath environments. The statistical results show the residuals vary from 0.5 mm to 2 mm with different signals and models of receivers. Short baseline tests show that in ideal conditions (i.e., a low multipath environment), using a single GNSS constellation (GPS, GLONASS, Galileo, or BDS) or their combinations can usually achieve millimetre-level precision and centimetre-level accuracy with almost 100% ambiguity success rates, regardless if the rover antenna is static or kinematic. In realistic condition (i.e. a severe multipath environment) the positioning precision and accuracy reduce to the centimetre level or even worse with a kinematic antenna. Multipath effect is one of the major error sources in GNSS positioning. Most of the currently available multipath detection and mitigation methods are based on stochastic modelling, advanced techniques in data processing, spatial geometry modelling, and special hardware designs. A new machine learning based multipath detection model is developed for undifferenced measurements (a single receiver approach). The approach is based on the fact that the multipath signature can be found in the multipath contaminated time series, and the features of multipath signature can be learned and identified by machine learning methods. The proposed model has been trained and validated with simulated data in an urban canyon environment with different satellite geometries. Moreover, the model has been trained and tested with real kinematic LoS and multipath data collected with a rotating arm rig in a multipath environment, and tested with multipath data collected near solar panels and near a building. The model has been tested using all available GNSS signals. The results show the model can achieve accuracy of 80%-90% with the simulated GNSS (GPS, Galileo, and BDS) data, and accuracy of 65%-70% with the real data collected using rotating arm rig on GPS L1 and GLONASS L1 signals. Real data collected near solar panels and near a building show that the well-trained model can achieve accuracy of about 60% in completely different multipath environments. The test results show the model was not well trained on GLONASS L2 and BDS data due to their carrier multipath errors are close to their carrier measurement error in ideal environment (low multipath environment)

Measurement signal quality assessment on all available and new signals of multi-GNSS (GPS, GLONASS, Galileo, BDS, and QZSS) with real data

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

Photonic Floquet time crystals

The public and scientists constantly have different perspectives. While on a time crystal, they stand in line and ask: What is a time crystal? Show me a material that is spontaneously crystalline in time? This study synthesizes a photonic material of Floquet time crystals and experimentally observes its indicative period-2T beating. We explicitly reconstruct a discrete time-crystalline ground state and reveal using an appropriately-designed photonic Floquet simulator the rigid period-doubling as a signature of the spontaneous breakage of the discrete time-translational symmetry. Unlike the result of the exquisite many-body interaction, the photonic time crystal is derived from a single-particle topological phase that can be extensively accessed by many pertinent nonequilibrium and periodically-driven platforms. Our observation will drive theoretical and technological interests toward condensed matter physics and topological photonics, and demystify time crystals for the non-scientific public.Comment: 39 pages, 5 figures, supplementary materials, 6 suppl. figure

Positioning buried utilities in difficult environments

Recently an increasing number of underground pipes have been established, particularly in city centres, for different applications such as sewage, electricity, gas, water and drainage. How to detect and make a precise 3-dimensional survey of buried pipelines has become a focused issue. This paper first of all reviews four trenchless technologies for locating buried utilities with an emphasis on describing their application and limitations. It is found that there is no single technology, which is able to locate all underground utility service infrastructures, particularly for deep buried plastic pipes. Meanwhile, these trenchless detection technologies need to be integrated with positioning technologies to create maps for buried utilities. One of the most attractive positioning technologies for providing absolute global position is Global Navigation Satellite Systems (GNSS). However a large percentage of buried utilities are in urban areas, where is not ideal for GNSS positioning technology. This paper evaluates the performance of single and multi GNSS constellations by carrying out a test in a controlled environment. The results show that using combined GNSS systems improve availability in urban canyons compared with using GPS alone. In addition, this paper describes an inertial based pipeline positioning technology called ‘Ductrunner’, which can locate and position the buried objects in spite of the material and depth without extra positioning systems. An approximately 30m long test pipeline has been established to evaluate the performance of Ductrunner. The maximum positioning errors are found to be 8cm in plan and 4cm in height. This shows that this technology is very promising for measuring deep pipes over relatively short distances

An autonomous ultra-wide band-based attitude and position determination technique for indoor mobile laser scanning

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

Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning

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