scholarly journals Performance Analysis of GPS/BDS Dual/Triple-Frequency Network RTK in Urban Areas: A Case Study in Hong Kong

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2437 ◽  
Author(s):  
Ying Xu ◽  
Wu Chen
Keyword(s):  
Hong Kong ◽  
Real Time ◽  
Urban Areas ◽  
Global Navigation Satellite Systems ◽  
Fixed Rate ◽  
Service Platform ◽  
Network Rtk ◽  
Triple Frequency ◽  
Gnss Network ◽  
Speed Accuracy

Network Real Time Kinematic (NRTK) positioning with instantaneous ambiguity resolution (AR) is currently one of the most popular techniques for real-time precise positioning using Global Navigation Satellite Systems (GNSS) carrier phase observations. Although NRTK has been successfully applied in many fields in surveying and navigation, the initialization speed, accuracy, and ambiguity successfully fixed rate of NRTK in urban areas (Hong Kong, for instance) would be significantly affected by blocked satellite signals. To address these problems and analyze the performance of GPS/BDS dual/triple-frequency NRTK in urban areas, we developed a new Hong Kong GNSS Network RTK Service Platform. Based on this platform, the performance of NRTK in urban areas was examined through a series of experiments. The results showed that: (1) The initialization time of the NRTK varied with the number of the visible satellite and the quality of the observation. (2) Centimeter-level NRTK service could be provided for users over Hong Kong using the Hong Kong GNSS Network RTK Service Platform. (3) In urban areas, GPS/BDS NRTK services for static, walking, and driving users significantly improved the ambiguity successfully fixed rate of the NRTK service when compared with that using the GPS signal alone. The NRTK ambiguity successfully fixed rate in Hong Kong was better than 99% in good environment. In typical urban environment, the RTK ambiguity successfully fixed rate with GPS/BDS was 33.4–72.4%, which was about 12.7–32.4% with GPS only. (4) BDS triple-frequency observation improved the initialization speed and positioning accuracy of RTK in Hong Kong.

Building spatial-temporal NO2 land use regression models in complex urban environment

2020 ◽  
Author(s):  
Peng Wei ◽  
Yang Xing ◽  
Li Sun ◽  
Zhi Ning
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Air Quality ◽  
Urban Environment ◽  
Real Time ◽  
Urban Areas ◽  
Personal Exposure ◽  
Pollutant Emission ◽  
Land Use Regression ◽  
Pollutant Distribution

<p>Air quality and traffic-related pollutants in urban areas are major concerns especially in meg-cities. Current Air Quality Monitoring Station (AQMS) cannot sufficiently reveal these pollution conditions with limited point measurements and limited information cannot supply adequate insight on personal exposure in a complex urban environment. Land Use Regression (LUR) model provided a feasible solution for estimating outdoor personal exposure by adding multiple data sources. However, fixed-site passive monitoring still lacks enough spatial coverage or spatial flexibility to estimate pollutant distribution at the fine-scale level.</p><p>A Mobile Air Sensor Network (MASEN) project was deployed in the Hong Kong area, with electrochemical gas sensors installed on the routine buses to capture on-road NO<sub>x</sub> pollutant measurement, the data was collected by the integrated sensor system and transfer to the database for real-time visualization. Compared with previous mobile measurements used for LUR model building which limited to 1-2 routes, this measurement covered major roads in the Hong Kong area and get an overview of pollutant distribution at various ambient. Two main variables were introduced to improve the model performance: 1) Sky View Factor (SVF) which represented pollutant dispersion status were obtained from Google street view image, a deep learning model was used for scene parsing to recognized targets in this procedure, 2) a Real-time Traffic Congestion Index (RTCI) which represented traffic pollutants emission was obtained from Google map and merged with road network. A common LUR model will be built based on a distance-decay regression selection strategy for variables selection. Meanwhile, a spatial-temporal LUR model will be built which contained both diurnal variability and day-to-day variability. Finally, a high-resolution pollution map of the urban areas will illustrate NO<sub>2</sub> pollutant distribution.</p><p>In this work, we aimed at estimating traffic-related pollutants in a complex city environment and identifying hotspots at both spatial and temporal aspects. Meanwhile, the novel data source which closely associated with traffic-related pollutant emission also gives a better understanding of guidance on urban planning.</p>

scholarly journals Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong

2019 ◽  
Vol 12 (1) ◽  
pp. 104 ◽  
Author(s):  
Qimin He ◽  
Kefei Zhang ◽  
Suqin Wu ◽  
Qingzhi Zhao ◽  
Xiaoming Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Hong Kong ◽  
Water Vapor ◽  
Wind Speed ◽  
Real Time ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
China Meteorological Administration ◽  
Super Typhoon

Typhoons can be serious natural disasters for the sustainability and development of society. The development of a typhoon usually involves a pre-existing weather disturbance, warm tropical oceans, and a large amount of moisture. This implies that a large variation in the atmospheric water vapor over the path of a typhoon can be used to study the characteristics of the typhoon. This is the reason that the variation in precipitable water vapor (PWV) is often used to capture the signature of a typhoon in meteorology. This study investigates the usability of real-time PWV retrieved from global navigation satellite systems (GNSS) for typhoons’ characterizations, and especially, the following aspects were investigated: (1) The correlation between PWV and atmospheric parameters including pressure, temperature, precipitation, and wind speed; (2) water vapor transportation during a typhoon period; and (3) the correlation between the movement of a typhoon and the transportation of water vapor. The case study selected for this research was Super Typhoon Mangkhut that occurred in mid-September 2018 in Hong Kong. The PWV time series were obtained from a conversion of GNSS-derived zenith total delays (ZTDs) using observations at 10 stations selected from the Hong Kong GNSS continuously operating reference stations (CORS) network, which are also located along the path of the typhoon. The Bernese GNSS Software (ver. 5.2) was used to obtain the ZTDs; and the root mean square (RMS) of the differences between the GNSS-ZTDs and International GNSS Service post-processed ZTDs time series was less than 8 mm. The RMS of the differences between the GNSS-PWVs (i.e., the ZTDs converted PWVs) and radiosonde-derived PWVs (RS-PWVs) time series was less than 2 mm. The changes in PWV reflect the variation in wind speed during the typhoon period to a certain degree, and their correlation coefficient was 0.76, meaning a significant positive correlation. In addition, a new approach was proposed to estimate the direction and speed of a typhoon’s movement using the time difference of PWV arrival at different sites. The direction and speed estimated agreed well with the ones published by the China Meteorological Administration. These results suggest that GNSS-derived PWV has a great potential for the monitoring and even prediction of typhoon events, especially for near real-time warnings.

scholarly journals A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning

2021 ◽  
Vol 13 (11) ◽  
pp. 2117
Author(s):  
Qi Cheng ◽  
Ping Chen ◽  
Rui Sun ◽  
Junhui Wang ◽  
Yi Mao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Urban Areas ◽  
Least Square ◽  
Global Navigation Satellite Systems ◽  
Positioning Accuracy ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Ground Vehicle ◽  
Vehicle Positioning

The performance requirements for Global Navigation Satellite Systems (GNSS) are becoming more demanding as the range of mission-critical vehicular applications, including the Unmanned Aerial Vehicle (UAV) and ground vehicle-based applications, increases. However, the accuracy and reliability of GNSS in some environments, such as in urban areas, are often affected by non-line-of-sight (NLOS) signals and multipath effects. It is therefore essential to develop an effective fault detection scheme that can be applied to GNSS observations so as to ensure that the vehicle positioning can be calculated with a high accuracy. In this paper, we propose an online dataset based faulty GNSS measurement detection and exclusion algorithm for vehicle positioning that takes account of the NLOS/multipath affected scenarios. The proposed algorithm enables a real-time online dataset based fault detection and exclusion scheme, which makes it possible to detect multiple faults in different satellites simultaneously and accurately, thereby allowing real-time quality control of GNSS measurements in dynamic urban positioning applications. The algorithm was tested with simulated/artificial step errors in various scenarios in the measured pseudoranges from a dataset acquired from a UAV in an open area. Furthermore, a real-world test was also conducted with a ground-vehicle driving in a dense urban environment to validate the practical efficiency of the proposed algorithm. The UAV based simulation exhibits a fault detection rate of 100% for both single and multi-satellite fault scenarios, with the horizontal positioning accuracy improved to about 1 metre from tens of metres after fault detection and exclusion. The ground vehicle-based real test shows an overall improvement of 26.1% in 3D positioning accuracy in an urban area compared to the traditional least square method.

scholarly journals A Real Time Localization System for Vehicles Using Terrain-Based Time Series Subsequence Matching

2020 ◽  
Vol 12 (16) ◽  
pp. 2607
Author(s):  
Hongjuan Zhang ◽  
Wenzhuo Li ◽  
Chuang Qian ◽  
Bijun Li
Keyword(s):  
Time Series ◽  
Real Time ◽  
Urban Areas ◽  
Position Estimation ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Subsequence Matching ◽  
Filter Model ◽  
Localization System ◽  
Road Map

Global Navigation Satellite Systems (GNSSs) are commonly used for positioning vehicles in open areas. Yet a GNSS frequently encounters loss of lock in urban areas. This paper presents a new real-time localization system using measurements from vehicle odometer data and data from an onboard inertial measurement unit (IMU), in the case of lacking GNSS information. A Dead Reckoning model integrates odometer data, IMU angular and velocity data to estimate the rough position of the vehicle. We then use an R-Tree structured reference road map of pitch data to boost spatial search efficiency. An optimized time series subsequence matching method matches the measured pitch data and the stored pitch data in reference road map for more accurate position estimation. The two estimated positions are fused using an extended Kalman filter model for final localization. The proposed localization system was tested for computational complexity with a median runtime of 12 ms, and for positioning accuracy with a median position error of 0.3 m.

Flood alleviation in a heavily urbanized area – Kai Tak Nullah, Hong Kong

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
Alan K. L. Chan ◽  
Colin K. C. Wong ◽  
Robin H. N. Lee ◽  
Mike W. H. Cho
Keyword(s):  
Hong Kong ◽  
Public Engagement ◽  
Urban Areas ◽  
Hydraulic Modelling ◽  
The Public ◽  
Design Considerations ◽  
Urbanized Area ◽  
Development Area ◽  
The Government ◽  
Exercise Design

The existing Kai Tak Nullah flows from Po Kong Village Road along Choi Hung Road and Tung Tau Estate into Kai Tak Development Area before discharging into the Victoria Harbour. Historically its upstream has been subject to flooding under storm conditions and this has had serious repercussions for the adjacent urban areas. A study has been commissioned by the Drainage Services Department of the Government of the Hong Kong Special Administrative Region (HKSAR), China to investigate the flood mechanisms and to provide flood alleviation measures by improving the capacity of the Kai Tak Nullah. In addition to flood alleviation, there is a strong public aspiration to rehabilitate the Kai Tak Nullah by a comparatively natural river design. Since the Kai Tak Nullah is located within a heavily urbanized area, traffic and environmental impacts are also highly concerned. The final flood alleviation scheme has thus had to strike a balance among the aforesaid factors with assistance from the hydraulic modelling utilizing InfoWorks Collection Systems (CS) software. This paper presents the public engagement exercise, design considerations, methodologies, and recommendations regarding the reconstruction and rehabilitation of the Kai Tak Nullah.

An expert system for real-time well field management

2012 ◽  
Vol 12 (5) ◽  
pp. 699-706 ◽  
Author(s):  
B. S. Marti ◽  
G. Bauser ◽  
F. Stauffer ◽  
U. Kuhlmann ◽  
H.-P. Kaiser ◽  
...  
Keyword(s):  
Drinking Water ◽  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
Urban Areas ◽  
Industrial Sites ◽  
Water Wells ◽  
Well Field ◽  
Field Management ◽  
Historical Management

Well field management in urban areas faces challenges such as pollution from old waste deposits and former industrial sites, pollution from chemical accidents along transport lines or in industry, or diffuse pollution from leaking sewers. One possibility to protect the drinking water of a well field is the maintenance of a hydraulic barrier between the potentially polluted and the clean water. An example is the Hardhof well field in Zurich, Switzerland. This paper presents the methodology for a simple and fast expert system (ES), applies it to the Hardhof well field, and compares its performance to the historical management method of the Hardhof well field. Although the ES is quite simplistic it considerably improves the water quality in the drinking water wells. The ES knowledge base is crucial for successful management application. Therefore, a periodic update of the knowledge base is suggested for the real-time application of the ES.

scholarly journals Improving the Stochastic Model of Ionospheric Delays for BDS Long-Range Real-Time Kinematic Positioning

2021 ◽  
Vol 13 (14) ◽  
pp. 2739
Author(s):  
Huizhong Zhu ◽  
Jun Li ◽  
Longjiang Tang ◽  
Maorong Ge ◽  
Aigong Xu
Keyword(s):  
Real Time ◽  
Long Range ◽  
Ionospheric Delay ◽  
Time Interval ◽  
Short Time Interval ◽  
Dual Frequency ◽  
Triple Frequency ◽  
Ambiguity Fixing ◽  
Power Spectral ◽  
Observation Noise

Although ionosphere-free (IF) combination is usually employed in long-range precise positioning, in order to employ the knowledge of the spatiotemporal ionospheric delays variations and avoid the difficulty in choosing the IF combinations in case of triple-frequency data processing, using uncombined observations with proper ionospheric constraints is more beneficial. Yet, determining the appropriate power spectral density (PSD) of ionospheric delays is one of the most important issues in the uncombined processing, as the empirical methods cannot consider the actual ionosphere activities. The ionospheric delays derived from actual dual-frequency phase observations contain not only the real-time ionospheric delays variations, but also the observation noise which could be much larger than ionospheric delays changes over a very short time interval, so that the statistics of the ionospheric delays cannot be retrieved properly. Fortunately, the ionospheric delays variations and the observation noise behave in different ways, i.e., can be represented by random-walk and white noise process, respectively, so that they can be separated statistically. In this paper, we proposed an approach to determine the PSD of ionospheric delays for each satellite in real-time by denoising the ionospheric delay observations. Based on the relationship between the PSD, observation noise and the ionospheric observations, several aspects impacting the PSD calculation are investigated numerically and the optimal values are suggested. The proposed approach with the suggested optimal parameters is applied to the processing of three long-range baselines of 103 km, 175 km and 200 km with triple-frequency BDS data in both static and kinematic mode. The improvement in the first ambiguity fixing time (FAFT), the positioning accuracy and the estimated ionospheric delays are analysed and compared with that using empirical PSD. The results show that the FAFT can be shortened by at least 8% compared with using a unique empirical PSD for all satellites although it is even fine-tuned according to the actual observations and improved by 34% compared with that using PSD derived from ionospheric delay observations without denoising. Finally, the positioning performance of BDS three-frequency observations shows that the averaged FAFT is 226 s and 270 s, and the positioning accuracies after ambiguity fixing are 1 cm, 1 cm and 3 cm in the East, North and Up directions for static and 3 cm, 3 cm and 6 cm for kinematic mode, respectively.

scholarly journals Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 657
Author(s):  
Aoki Takanose ◽  
Yoshiki Atsumi ◽  
Kanamu Takikawa ◽  
Junichi Meguro
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Estimation Method ◽  
Tall Buildings ◽  
Autonomous Driving ◽  
Reliability Estimation ◽  
Height Variation ◽  
Position Information ◽  
Real Time Kinematic ◽  
Evaluation Test

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

Development of an in-house real-time pcr to monitor the prevalence of macrolide-resistant Mycoplasma pneumoniae in Hong Kong

Pathology ◽  
2017 ◽  
Vol 49 ◽  
pp. S116
Author(s):  
Ho-Yin Lam ◽  
Thomas Hin-Ching Wong ◽  
Anthony Tsz-Chun Wong ◽  
Gilman Kit-Hang Siu ◽  
Wing-Cheong Yam ◽  
...  
Keyword(s):  
Hong Kong ◽  
Real Time ◽  
Mycoplasma Pneumoniae ◽  
Real Time Pcr
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