Lawrence Lau

Investigations into Phase Multipath Mitigation Techniques for High Precision Positioning in Difficult Environments

The modelling of most Global Navigation Satellite System (GNSS) errors/biases and developments of data processing techniques have been improved substantially since the birth of Global Positioning System (GPS), however, there has been much less progress in the improvement of phase multipath mitigation techniques. Multipath therefore remains one of the most important error sources in high precision GNSS positioning. This is because multipath is site-dependent and therefore cannot be eliminated by differencing techniques. Also multipath is highly dependent on satellite-reflector-antenna geometry, which usually causes rapid changes in phase multipath errors especially in Real-Time Kinematic (RTK) applications. Multipath mitigation for static antennas such as those at reference stations can be carried out by site calibration, averaging over long observation times, and through the estimation of the error using filtering based on signal-to-noise ratio (SNR) data. However, multipath mitigation for kinematic antennas is still very difficult today. Nevertheless, much research has been carried out on a particular class of phase multipath mitigation techniques: ones that can be applied within positioning algorithms (rather than incorporated into the receiver tracking loops or antennas). This paper investigates and further develops a number of state-of-the-art techniques in this category. They include phase multipath estimation using SNR data, phase multipath estimation through the use of closely spaced antennas, multipath mitigation stochastic models such as the satellite elevation angle model and SNR-based models (SIGMA-e model and our modified SNR-based model), and our own novel ray-tracing method. The techniques are tested with both real and simulated data, the real test datasets have been collected on the Laboratoire Central des Ponts et Chaussees (LCPC) testbed near Nantes in France, and on the campus of the University of Nottingham during SPACE data collection experiments.

Comparison of measurement and position domain multipath filtering techniques with the repeatable GPS orbits for static antennas

Abstract Repeatable satellite orbits can be used for multipath mitigation in Global Position System (GPS) based deformation monitoring and other high precision GPS applications that involve continuous observation with static antennas. Multipath signals repeat when the GPS constellation does. There are two possible domains for repeat-time based multipath filtering. One is based on filtering multipath errors in the measurements and the other on filtering multipath contaminated positioning errors. If all satellites have the same repeat time, the performance in the two domains should be very similar. However, the repeat time for individual satellite may be different and this leads to possible differences in performance in the two filtering domains. The aim of this paper is to assess the performance of the two filtering domains on multipath mitigation for short baselines. Three roof-top data sets collected at University College London and a data set collected at two International GNSS Service high-rate stations are used in our performance assessment. Test results are analysed and insights into the two filtering domains are described in details in the paper. Our overall result shows that the performances of the two filtering domains on multipath mitigation are similar, which is about 40% improvement on positioning accuracy when comparing with no multipath filtering.

Flight Tests of Error-Bounded Heading and Pitch Determination with Two GPS Receivers

This paper describes a heading and pitch determination algorithm using single-epoch measurements from two GPS receivers. The algorithm aims to provide accurate heading and pitch solutions with controlled quality. Since the separation of the two antennas (baseline length) is known, this length has been used in the algorithm to constrain the heading and pitch solutions in the final stage of the estimation process. Moreover, error bounds (EB) of heading and pitch solutions are calculated at each epoch in order to provide statistical bounds for their errors. The proposed algorithm has been tested with high (frequent turns with quick change in pitch angle to ±50° and bank angle to ±80°) and low dynamic flight test data collected in Germany and the solutions are compared with high-accuracy heading and pitch provided by a high-grade GPS/INS system. Test results of achieved accuracies and success rate of bounds on heading and pitch errors are described. This attitude determination work is part of the ANASTASIA (Airborne New Advanced Satellite techniques & Technologies in A System Integrated Approach) project, which was a 6th Framework programme of European Commission Project that was led by Thales Avionics, France. It has been found that about 96% of accepted solutions in the low dynamic data sets have heading and pitch errors less than the ANASTASIA required accuracy of 0.4°. Moreover, the rms error and error bounding analyses of the results show that the performance of the heading and pitch algorithm on the low dynamic data sets is better than that of the high dynamic data sets. Extensive analysis of the relatively poor performance on the high dynamic data sets is described and the problems/error sources are identified and discussed.

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.

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.

Investigation into the Effect of Atmospheric Particulate Matter (PM2.5 and PM10) Concentrations on GPS Signals

The Global Positioning System (GPS) has been widely used in navigation, surveying, geophysical and geodynamic studies, machine guidance, etc. High-precision GPS applications such as geodetic surveying need millimeter and centimeter level accuracy. Since GPS signals are affected by atmospheric effects, methods of correcting or eliminating ionospheric and tropospheric bias are needed in GPS data processing. Relative positioning can be used to mitigate the atmospheric effect, but its efficiency depends on the baseline lengths. Air pollution is a serious problem globally, especially in developing countries that causes health problems to humans and damage to the ecosystem. Respirable suspended particles are coarse particles with a diameter of 10 micrometers or less, also known as PM10. Moreover, fine particles with a diameter of 2.5 micrometers or less are known as PM2.5. GPS signals travel through the atmosphere before arriving at receivers on the Earth’s surface, and the research question posed in this paper is: are GPS signals affected by the increased concentration of the PM2.5/PM10 particles? There is no standard model of the effect of PM2.5/PM10 particles on GPS signals in GPS data processing, although an approximate generic model of non-gaseous atmospheric constituents (<1 mm) can be found in the literature. This paper investigates the effect of the concentration of PM2.5/PM10 particles on GPS signals and validates the aforementioned approximate model with a carrier-to-noise ratio (CNR)-based empirical method. Both the approximate model and the empirical results show that the atmospheric PM2.5/PM10 particles and their concentrations have a negligible effect on GPS signals and the effect is comparable with the noise level of GPS measurements.

Analysing the Zenith Tropospheric Delay Estimates in On-line Precise Point Positioning (PPP) Services and PPP Software Packages

As Global Navigation Satellite System (GNSS) signals travel through the troposphere, a tropospheric delay occurs due to a change in the refractive index of the medium. The Precise Point Positioning (PPP) technique can achieve centimeter/millimeter positioning accuracy with only one GNSS receiver. The Zenith Tropospheric Delay (ZTD) is estimated alongside with the position unknowns in PPP. Estimated ZTD can be very useful for meteorological applications, an example is the estimation of water vapor content in the atmosphere from the estimated ZTD. PPP is implemented with different algorithms and models in online services and software packages. In this study, a performance assessment with analysis of ZTD estimates from three PPP online services and three software packages is presented. The main contribution of this paper is to show the accuracy of ZTD estimation achievable in PPP. The analysis also provides the GNSS users and researchers the insight of the processing algorithm dependence and impact on PPP ZTD estimation. Observation data of eight whole days from a total of nine International GNSS Service (IGS) tracking stations spread in the northern hemisphere, the equatorial region and the southern hemisphere is used in this analysis. The PPP ZTD estimates are compared with the ZTD obtained from the IGS tropospheric product of the same days. The estimates of two of the three online PPP services show good agreement (<1 cm) with the IGS ZTD values at the northern and southern hemisphere stations. The results also show that the online PPP services perform better than the selected PPP software packages at all stations.

A theoretical and empirical integrated method to select the optimal combined signals for geometry-free and geometry-based three-carrier ambiguity resolution

Twelve GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals (L1, L2, L5). Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition.

Analysis and machine-learning based detection of outlier measurements of ultra-wideband in an obstructed environment

Indoor positioning technologies have been widely used in many industrial applications such as intelligent inventory management and assembly control. Ultra-Wide Band (UWB) can provide sub-metre level positioning accuracy at a distance of several dozen metres with high robustness. However, UWB measurements can be contaminated by reflected, refracted and deflected signal in practice, the contaminated measurements are outliers in data processing and degrade the positioning performance if they are not treated properly. In indoor environments, UWB signals may penetrate some structures/materials and these refracted signals are outliers in data processing for position determination. This paper investigates the statistical distribution of errors due to refracted/penetrated signals. Classification and Regression random forests are used to detect outlier measurements and apply error mitigation, respectively. Two datasets are collected to cross-validate the proposed method. The results show that the proposed method can achieve a detection accuracy of about 80%. Besides, the datasets show that rejecting detected outlier measurements and applying error mitigation can improve distance measurement accuracy by 80%.