Journal of Global Positioning Systems

Vol. 18, No. 2, 2022

Cover

JGPS Team Structure, Copyright and Table of Contents

1. GNSS-IR Inshore Tide Measurement Based on Low-Cost Chipset

2. Photorealistic simulation platform for autonomous landing of fixed-wing aircraft

3. GPS/MEMS IMU/UWB tightly coupled integrated navigation with robust Kalman filter based on bifactor

4. Two-stage fusion localization based on UWB/PDR/Geomagnetism in underground space

ABSTRACTS OF PHD DISSERTATIONS

5. Ocean Wind Vector Retrieval Based on Spaceborne Global Navigation Satellite System Reflectometry

Guodong Zhang

See Abstract and PDF file

Global Navigation Satellite System Reflectometry (GNSS-R) uses GNSS signals as the microwave remote sensing signal source to detect physical parameters of the global surface. GNSS-R is an organic fusion of navigation and remote sensing, an innovative application of GNSS signals. GNSS-R has the advantages of all-weather, multiple signal sources, high spatial and temporal resolution, which is beneficial to be carried on microsatellite platforms. The ocean wind vector is an important part of ocean dynamic parameters. Accurate ocean wind vector detection plays a vital role in the early warning and forecasting of marine dynamic disasters. The research of ocean wind vector retrieval using spaceborne GNSS-R is helpful for the realization of global high temporal and spatial resolution ocean wind detection, which has important practical significance and urgent national needs. Although a lot of research and progress has been made in ocean wind vector retrieval using spaceborne GNSS-R, there are still many problems before commercial application, such as wind speed accuracy and wind direction retrieval, which hinder its popularization and application. Aiming at these problems, the main research contents and achievements of this thesis are as follows: (1) In order to make spaceborne GNSS-R cyclone wind research get rid of the dependence on the prior information of whether the cyclone event occurs, a single-pass cyclone event detection algorithm using spaceborne GNSS-R full delay-Doppler map (DDM) is presented. The study focuses on investigating the influence of cyclone on the spaceborne GNSS-R full DDM. An observable is defined to describe full DDM asymmetry, and demonstrated to be sensitive to cyclone event using the simulator. The proposed method is based on a time sliding window to detect the full DDM asymmetry anomalies. The results show that cyclone event can be detected by the algorithm. These results provide information to guide the high wind speed retrieval in real-time using spaceborne GNSS-R. (2) Spaceborne GNSS-R ocean wind speed retrieval has the problems of high data quality control standards and low accuracy of high wind speed. Based on the cyclone event detection algorithm, an 135 ocean wind speed retrieval algorithm based on ocean state is presented. The ocean state types are divided into conventional ocean state and cyclone ocean state. Before wind speed retrieval, the cyclone monitoring algorithm is used to identify the ocean state type. Then, the ocean wind speed is retrieved using the empirical geophysical model function of different ocean state types. The retrieval accuracy of high wind speed is improved while the retrieval accuracy of medium and low wind speed is ensured. The wind speed retrieval algorithm is suitable for spaceborne real-time retrieval. (3) There are few studies on the ocean wind direction retrieval using spaceborne GNSS-R, because the specular reflection signal is not sensitive to the sea surface wind direction. The wind direction retrieval algorithms using spaceborne GNSS-R in non-specular geometry are presented. The sensitivity of the scattered GNSS signal in the non-specular geometry to wind direction is analyzed. The sub-satellite non-specular observation mode is constructed. The observable that is sensitive to wind direction in this mode is defined. A wind direction retrieval algorithm based on wind speed and a wind vector retrieval algorithm based on maximum likelihood estimation are constructed. It solves the problem that the specular reflection signal is difficult to retrieve the wind direction and can achieve real-time wind direction retrieval using spaceborne GNSS-R. (4) In order to meet the needs of real-time processing and fast retrieval of ocean wind vector using spaceborne GNSS-R, the spaceborne GNSS-R fast retrieval software for ocean wind vector is designed and implemented. The data processing system including cyclone event detection, ocean wind speed retrieval and ocean wind direction retrieval is constructed. It can provide a reliable and stable application software for the research and application of ocean wind vector real-time retrieval using spaceborne GNSS-R

6. Enhancing the Accuracy of Water Vapour Retrieval from Remote Sensing Observations Using Ground-based GNSS Data

Jia He

See Abstract and PDF file

Water vapour, as the fundamental element and one of the most important natural greenhouse gases in the atmosphere, is vital for heat and moisture fluxes. Improved knowledge of water vapour and its variability on the different temporal-spatial scales is essential for climate and environmental research. Water vapour content can be estimated through radiosonde balloons, microwave-radiometers, sunphotometers, Global Navigation Satellite Systems (GNSS) / Global Positioning Systems (GPS), and remote sensing satellites. These observation instruments provide products with different but complementary characteristics. For instance, radiosonde and GNSS/GPS observations are usually considered as ground truth because of their high precision, but their applications are restricted by the locations of the stations. Remote sensing observation, on the other hand, is the most efficient means of water vapour observation on the global scale but with a larger retrieval error. The core research aim is to enhance the water vapour retrieval accuracy from remote sensors. Special weight was put on water vapour observation from Near Infrared (NIR) channels. A novel retrieval method has been developed for the Moderate Resolution Imaging Spectro-radiometer (MODIS) onboard the Terra and Aqua satellite platforms in this research based on empirical regression analysis. This new approach provides an effective way to retrieve water vapour without preobtained atmospheric information. Water vapour data observed during 2003 ~ 2017 from 464 GPS stations located in western North America and their spatialtemporal collocated MODIS level 1 reflectance data were employed as training data for model development. The training data were resampled into 10 subsets using the bootstrap method. The regression functions trained by these independent subsets reduced the uncertainty in the model training and minimized the sensitivity of possible channel drifting. Verifications in North America show that the root mean square error (RMSE) for water vapour calculated from MODIS/Terra reduces 48.12% to 2.362 mm when using two-channel ratio transmittance and 50.74% to 2.243 mm when using three-channel ratio transmittance. The RMSE for water vapour calculated from MODIS/Aqua reduces 42.54% to 2.562 mm and 42.99% to 2.541 mm when using two-channel and three-channel ratio transmittance, respectively. Validations over five additional stations also show that the overall RMSE for MODIS/Terra data reduces 22.80% to 5.946 mm when using two-channel ratio transmittance, and 21.69% to 6.006 mm when using three-channel ratio 137 transmittance. For MODIS/Aqua data, the reductions are 16.42% to 6.010 mm when using two-channel ratio transmittance and 15.26% to 6.094 mm when using three-channel ratio transmittance. The retrieval algorithm was also validated in Australia and its neighbouring area for the first time. The observation results over 2017 ~ 2019 have clearly shown that our new ensemble-based empirical regression model, which was developed using data from the North Hemisphere, is still valid in the South Hemisphere. For the data obtained from MODIS/Terra, the RMSE has reduced by 58.53% from 5.712 mm to 2.369 mm when using 2-channel ratio transmittance and has reduced by 56.14% to 2.505 mm using 3-channel ratio transmittance, respectively. For the data obtained from MODIS/Aqua, the RMSE has reduced by 49.17% from 5.170 mm to 2.628 mm using 2-channel ratio transmittance and has reduced by 46.60% to 2.761 mm using 3-channel ratio transmittance, respectively. The results further prove that the newly proposed retrieval model has very good property of having no temporal or spatial dependency over a large observation area. The coefficients can be easily applied to areas of interest without pre-calculated input parameters of atmospheric profiles. It is reasonable to conclude that this algorithm provides an effective way to retrieve water vapour globally under cloud-free condition. On the other hand, as the surface spectral reflectance is one of the error sources for water vapour retrieval, regression functions trained for different land cover types adapted from MCD12Q1 IGBP legend are discussed. Thus, the bias for MODIS NIR channels could be further reduced. Validations in North America show that for data calculated from MODIS/Terra, the RMSE reduced 50.78% to 2.229 mm for data using two-channel ratio transmittance and 53.06% to 2.126 mm for data using three-channel ratio transmittance. For data obtained from MODIS/Aqua, the RMSE reduced 45.54% to 2.423 mm when using two-channel ratio transmittance and 45.34% to 2.432 mm when using three-channel ratio transmittance. Last but not least, the empirical regression method was implemented for water vapour observation from MERSI/FY-3B NIR channels. The collocated MERSI L1b reflectance data in the NIR channels are used for water vapour retrieval. PWV data observed from 256 ground-based GPS stations located in the western North America in 2016 are used as reference data for model development. Then, validation is performed with data obtained during 2017 ~ 2019 from both the western North America and Australia to assess the performance of the proposed algorithm. The results indicate that the new PWV results agree very well with ground based PWV reference data. The mean absolute percentage error (MAPE) for ensemble median PWV is 16.72% ~ 36.74% in western North America and is 15.47% ~ 32.31% in Australia. The RMSE is 4.635 mm ~ 8.156 mm in western North America and is 5.383 mm ~ 8.900 mm in Australia. The weighted mean value using three-channel ratio transmittance has the best retrieval accuracy, with RMSE of 4.635 mm in western North America and 5.383 mm in Australia. Together with MERSI onboard of other FY series, more information on water vapour distribution on the global scale would be provided for climate and environmental research

7. GNSS PPP/INS Integrated Precise Positioning and Attitude Determination with Comprehensive Error Analysis

Lingxuan Wang

See Abstract and PDF file

GNSS Precise Point Positioning (PPP) has attracted widespread attention for its advantages of low cost and high precision at wide-area. However, the solution of PPP ambiguity parameters requires a long convergence time. Due to the inherent vulnerability of GNSS, frequent re-initialization under dynamic conditions seriously affects its positioning and navigation performance. To meet the current requirements, a multisensor integrated approach with high precision, high reliability, and high integrity has become a necessary trend for the development of the industry. Inertial Navigation System (INS) has the advantage of high precision in the short term, so it is complementary to GNSS and provides a solution to overcome the shortcomings of PPP in dynamic situations. As an essential scope of the multi-sensors integrated navigation systems research, PPP/INS has been studied and applied extensively. However, several core problems still need to be solved, mainly reflected in the high-reliability algorithms, weight determination of different systems, comprehensive error analysis methods, quality control and accuracy evaluation methods for the filtering process, and so on. Such problems are bottlenecks restricting the development and application of highprecision PPP/INS integrated systems, which have been the research hotspots and challenging topics in recent years. This thesis researches the problems mentioned above, and the main contents and contributions can be summarized as follows. (1) Cycle slip detection is the premise to ensure the high precision positioning performance of PPP. Thus, a detailed error analysis is carried out for the INS-aided cycle-slip detection term. The specific influence of INS error on the cycle-slip detection term and detection performance are revealed, and the INS-aided cycle-slip detection terms suitable for PPP dynamic navigation application are constructed. Furthermore, a targeted detection threshold is derived. Experimental results show that the proposed algorithm can enhance the performance of PPP cycle-slip detection and reduce the risk of false alarms and missing detection. (2) A PPP ambiguity resolution enhancement algorithm is proposed based on the virtual observation equation with INS position constrained. When GNSS signals are recaptured, the ambiguity parameters need to be reset. And the error accumulation of INS during the GNSS lock-out period will significantly affect the refixation of ambiguity parameters. From the error propagation analysis of INS independent navigation, a stochastic model for the virtual observation equation with INS position-constrained is constructed according to the time 139 of loss of lock. The stochastic model can more accurately reflect the characteristics of INS errors so that INS can always play a positive role in PPP ambiguity resolution process. Even if the INS error reaches several meters, it will not degrade the performance of the PPP itself. Based on this research, the enhanced ambiguity resolution strategy for the integrated system is discussed. And the PPP/INS integrated ambiguity resolution enhancement algorithm is subsequently proposed. Several measured vehicle navigation data have been used to evaluate the algorithm. (3) The traditional Kalman filtering method only relies on the innovation vector for quality control and cannot fully consider the filter modeling error. A novel Kalman filtering process is used to achieve the unified posterior estimation of process noise, observation noise, and innovation vector. And the redundant observation factor of each noise can be accurately calculated by this process. A variance component estimation (VCE) method for the Kalman filter is proposed based on this, which comprehensively considers the redundant observation factors of various noise items and solves the problem of oversimplification of the previous VCE methods. The proposed method can directly estimate the variance components of the multi-source observations in the filter and realize the weight determination of the multi-source information. Taking multi-frequency and multi-system GNSS PPP static positioning as an example, the experimental analysis is carried out. The results show that the variance-covariance matrix of various observation and state-predicted values can be reasonably adjusted according to the calculated process noise residuals, observation residuals, and innovation vectors. And the positioning accuracy is improved significantly. (4) A novel GNSS PPP/INS integration strategy is proposed employing the rigid body nonlinear kinematic equation. In the current GNSS/INS integrated filtering model, the observations of the two different systems are processed in the time update and measurement update processes, respectively. And the INS observation with errors directly constitutes the state transition matrix, which brings systematic errors but cannot be effectively eliminated. At the same time, the two systems cannot achieve the posterior estimation of the variancecovariance components. It is the only way to balance the contributions of different systems by tedious adjusting them through prior information and experience values. This thesis constructs a novel PPP/INS integrated state model and observation equation using the direct Kalman filtering method considering the kinematic relationship between state parameters. The new approach is more in line with the Kalman filtering theory. It has a more straightforward structure, which is convenient for providing dynamic model constraints for INS. It also can significantly reduce the impact of random drift of INS on the integrated system. At the same time, it is convenient to obtain a comprehensive error analysis of each noise in the filtering process and solve the weight determination problem for the different types of observation in the integrated system.

8. An Investigation of Real-time GNSS Precipitable Water Vapor Retrievals

Peng Sun

See Abstract and PDF file

Water vapor, the content of which can be measured by precipitable water vapor (PWV), is a greenhouse gas in the troposphere, a carrier of atmospheric energy exchange, and a material basis of weather changes. When the signals of Global Navigation Satellite Systems (GNSS) propagate through the troposphere, the tropospheric delay, as a major error source of GNSS positioning, occurs resulting from both the dry component and water vapor in the troposphere. As a result, the water vapor information is embedded in the GNSS signals, and all-weather high-accuracy tropospheric delay and PWV products can be obtained from GNSS data processing for weather and climate change research, which is low-cost compared to other water vapor monitoring techniques. For the purpose of time-critical extreme weather prediction, GNSS real-time precise point positioning (PPP) has become a powerful technique for the determination of the zenith tropospheric delay (ZTD) over a GNSS station of interest, and the subsequent high-accuracy retrieval of PWV. This paper mainly focuses on the high-accuracy atmospheric modeling, development of real-time PPP software and the assessment of accuracy of the resulted ZTD, refinement of ZHD interpolation method for obtaining highaccuracy ZHD (zenith hydrostatic delay) from VMF1/VMF3 forecasting grids, ZWD (zenith wet delay)-PWV conversion model and the accuracy of PWV resulting from the VMF1/VMF3-based ZHD and the conversion model, and the relation between the real-time GNSS-PWV and weather changes. The details are as follows: (1) In most of the empirical models, atmospheric pressure at the user site is typically obtained from atmospheric pressure at a reference height combined with an atmospheric pressure vertical reduction model. If the reference height largely differs from the height of the user site, the quality of the predicted atmospheric pressure is usually poor due to the simple reduction model used. In this study, a voxel-based atmospheric pressure model, named PVoxel, was developed for obtaining better accuracy atmospheric pressures using sample data of 10-year ERA5 monthly mean data, totaling 120 monthly mean values in the temporal domain. Each monthly mean atmospheric pressure and virtual temperature at each of the four selected reference heights over all globally distributed grid points (horizontal resolution: 1°?1°), i.e., at the nodes of the 3D voxels, were determined. Then the characteristics of the annual and semi-annual variation in both atmospheric pressure and virtual temperature in the temporal domain for each node were modeled. The PVoxel model developed is 4-dimensional, thus it can be used to predict atmospheric pressure at a given geographic position and any altitude, and any time. The model was evaluated by comparing the atmospheric pressures predicted for the sites of all globally distributed radiosonde stations against their corresponding radiosonde data of the sites. The model predicted results were also compared with that of the 141 widely used models like GPT3, UNB3m et al. and the comparison results showed that PVoxel outperformed these models, especially at high altitudes. The significant performance improvement of the new model is promising for an improvement in its resultant ZHD, which is significant for obtaining more accurate position (especially the height component) and zenith tropospheric delay. (2) A modified BNC software (BNC_MET) was developed for real-time retrieval of GNSS-PWV. Compared to the original open-source BNC software, the error correction, quality control and parameter estimation modules were significantly improved in the new development. Two experiments were designed for the evaluation of real-time ZTD estimated by the modified BNC software. Firstly, the accuracy of ZTD resulting from four different real-time service (RTS) products were evaluated using GPS-only and GPS+GLONASS real-time PPP. Compared to the IGS final tropospheric products, the mean RMSE of ZTD resulting from GPS-only and GPS+GLONASS PPP using CNES RTS products were 8.4 mm and 8.1 mm, respectively, while the corresponding RMSEs were 7.4 mm and 7.0 mm compared to the CODE tropospheric products. High accuracy ZTD (but slightly worse than the CNES ones) were also obtained from PPP using GFZ and WHU products, and the IGS03 performed the worst. Secondly, the accuracy of ZTD estimated by BDS-only PPP and GPS+BDS PPP were also evaluated using GFZ RTS products. The results showed that the accuracy of BDS-only PPP-resulted ZTD was slightly worse than that of GPS-only ones, and the GPS+BDS scheme performed better than GPS-only and BDS-only schemes. (3) Refinement of ZHD interpolation method for VMF1/VMF3 forecasting grids. VMF1 and VMF3 forecasting grids provide ZHD, ZWD and mapping function coefficients at globally distributed grid points. However, a unified atmospheric pressure vertical correction coefficient was adopted by the official code provided by the data-provider. As a result, large ZHD prediction errors were obtained in some places. The ZHD vertical correction part during the interpolation was improved by two new methods. Firstly, atmospheric pressure vertical correction coefficient at each of the grid point were fitted and modeled for the vertical correction of VMF-based ZHD. Secondly, 3Dvoxel based atmospheric temperature model from the above-mentioned PVoxel model were used for the ZHD vertical correction. The newly proposed methods were evaluated by surface atmospheric pressure observations from 2019 to 2021 at 404 radiosonde stations. For the first method, the mean RMSE of ZHD interpolated from VMF1, VMF3(5°?5°) and VMF3(1°?1°) forecasting grids were reduced from 5.5 mm, 4.9 mm and 3.9 mm to 3.7 mm, 4.2 mm and 3.6mm, respectively, while the maximum RMSE were reduced from 4.01 cm, 4.24 cm and 1.95 cm to 1.63 cm, 2.38 cm and 1.83 cm, accordingly. For the second method, the mean RMSEs were reduced to 3.6, 4.3 and 3.7 mm, respectively, and maximum ones were reduced to 1.64, 2.38 and 1.83 cm, respectively. Both the two newly proposed methods outperformed the traditional method. In addition, three horizontal interpolation methods were also evaluated, and the bilinear interpolation performed the best. (4) A new weighted mean temperature ( Tm ) model, named GGNTm, considering the nonlinear variation of Tm in the vertical direction was established using 10-year long ERA5 monthly-mean reanalysis data. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modeled with the variables of the mean, annual, and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year: (1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5°?5°; (2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted Tm values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and 142 mean RMSE of the model-predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively; the mean bias and mean RMSE of the model’s Tm values over all pressure levels in the height range from the surface to 10 km altitude were 0.16 and 4.20 K, respectively. Results indicated that significant improvements made by the new model were at high-altitude pressure levels. (5) The accuracy of the GNSS-retrieved real-time PWV using ZHD from VMF forecasting grid and ???? from GGNTm was also investigated. GPS observations from 41 IGS stations that have co-located radiosonde stations during the period of the first half of 2020 were used to test the quality of GPS-PWV. The results showed that mean RMSE of the PWVs resulting from GPS-PPP was smaller than 2mm compared to reference PWVs from collocated radiosonde data, which is accurate enough for meteorological applications. (6) The correlation between real-time GNSS-PWV and weather change was analyzed using 20-day-long real-time PWV obtained from 11 CORS stations in Hong Kong. The atmospheric pressures measured by co-located meteorological sensors were used for the calculation of ZHD and GGNTm model was used for the conversion of ZWD to PWV. By comparing the real-time PWV to the weather records provided by Hong Kong Observatory, it can be concluded that the real-time PWVs are tightly correlated to weather change. The accuracy of real-time PWV can be improved by implementing the above-mentioned research, which may make significant contributions to weather forecasting and the time-critical severe weather monitoring. This dissertation includes 56 figures, 23 tables and 203 references.

Back Cover

CPGPS, 2022. All the rights reserved.

Last Modified: July, 2023