scholarly journals High-Accuracy Real-Time Kinematic Positioning with Multiple Rover Receivers Sharing Common Clock

2021 ◽  
Vol 13 (4) ◽  
pp. 823
Author(s):  
Lin Zhao ◽  
Jiachang Jiang ◽  
Liang Li ◽  
Chun Jia ◽  
Jianhua Cheng
Keyword(s):  
Real Time ◽  
Ambiguity Resolution ◽  
Temporal Stability ◽  
Estimation Method ◽  
Satellite System ◽  
Integer Ambiguity ◽  
Dual Frequency ◽  
Kinematic Positioning ◽  
Real Time Kinematic ◽  
Satellite Visibility

Since the traditional real-time kinematic positioning method is limited by the reduced satellite visibility from the deprived navigational environments, we, therefore, propose an improved RTK method with multiple rover receivers sharing a common clock. The proposed method can enhance observational redundancy by blending the observations from each rover receiver together so that the model strength will be improved. Integer ambiguity resolution of the proposed method is challenged in the presence of several inter-receiver biases (IRB). The IRB including inter-receiver code bias (IRCB) and inter-receiver phase bias (IRPB) is calibrated by the pre-estimation method because of their temporal stability. Multiple BeiDou Navigation Satellite System (BDS) dual-frequency datasets are collected to test the proposed method. The experimental results have shown that the IRCB and IRPB under the common clock mode are sufficiently stable for the ambiguity resolution. Compared with the traditional method, the ambiguity resolution success rate and positioning accuracy of the proposed method can be improved by 19.5% and 46.4% in the restricted satellite visibility environments.

A novel partial ambiguity method for multi-GNSS real-time kinematic positioning

2021 ◽  
pp. 1-14
Author(s):  
Haiyang Li ◽  
Guigen Nie ◽  
Jing Wang ◽  
Shuguang Wu ◽  
Yuefan He
Keyword(s):  
Approximate Solution ◽  
Real Time ◽  
Ambiguity Resolution ◽  
Integer Ambiguity ◽  
Resolution Method ◽  
Kinematic Positioning ◽  
Partial Ambiguity Resolution ◽  
Resolution Process ◽  
Real Time Kinematic ◽  
Ambiguity Success Rate

Abstract Recent progress in using real-time kinematic (RTK) positioning has motivated the exploration of its application due to its high accuracy and efficiency. However, poorly-observed satellite data will cause unfixed ambiguities and markedly biased solutions. A novel partial ambiguity resolution method, named the irrespective of integer ambiguity resolution (IIAR) model, is proposed and applied to improve the reliability of ambiguity resolution. The proposed method contains initial ambiguity resolution and irrespective of integer ambiguity processes. The initial ambiguity resolution process applies an iterative partial ambiguity resolution method to obtain an approximate solution. The irrespective of integer ambiguity process transforms the approximate solution to a high-precision solution. Experiments show that the approximate solution is unreliable when the initial ambiguity resolution process has small redundancy, and the proposed method can obtain better results for those cases. The IIAR method showed about a 40% improvement of multi-GNSS ambiguity success rate and about a 25% improvement of standard deviation. Therefore, these results show that the proposed IIAR method can improve the results of multi-GNSS RTK positioning significantly.

scholarly journals Real-Time PPP-RTK Performance Analysis Using Ionospheric Corrections from Multi-Scale Network Configurations

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3012 ◽  
Author(s):  
Dimitrios Psychas ◽  
Sandra Verhagen
Keyword(s):  
Real Time ◽  
Ambiguity Resolution ◽  
Ionospheric Disturbance ◽  
The United States ◽  
Integer Ambiguity ◽  
Convergence Time ◽  
Dual Frequency ◽  
Multi Scale ◽  
Time Required ◽  
The Impact

The long convergence time required to achieve high-precision position solutions with integer ambiguity resolution-enabled precise point positioning (PPP-RTK) is driven by the presence of ionospheric delays. When precise real-time ionospheric information is available and properly applied, it can strengthen the underlying model and substantially reduce the time required to achieve centimeter-level accuracy. In this study, we present and analyze the real-time PPP-RTK user performance using ionospheric corrections from multi-scale regional networks during a day with medium ionospheric disturbance. It is the goal of this contribution to measure the impact the network dimension has on the ambiguity-resolved user position through the predicted ionospheric corrections. The user-specific undifferenced ionospheric corrections are computed at the network side, along with the satellite phase biases needed for single-receiver ambiguity resolution, using the best linear unbiased predictor. Such corrections necessitate the parameterization of an estimable user receiver code bias, on which emphasis is given in this study. To this end, we process GPS dual-frequency data from four four-station evenly distributed CORS networks in the United States with varying station spacings in order to evaluate if and to what extent the ionospheric corrections from multi-scale networks can improve the user convergence times. Based on a large number of samples, our experimental results showed that sub-10 cm horizontal accuracy can be achieved almost instantaneously in the ionosphere-weighted partially-ambiguity-fixed kinematic PPP-RTK solutions based on corrections from a network with 68 km spacing. Most of the solutions (90%) were shown to require less than 6.0 min, compared to the ionosphere-float PPP solutions that needed 68.5 min. In case of sparser networks with 115, 174 and 237 km spacing, 50% of the horizontal positioning errors are shown to become less than one decimeter after 1.5, 4.0 and 7.0 min, respectively, while 90% of them require 10.5, 16.5 and 20.0 min. We also numerically demonstrated that the user’s convergence times bear a linear relationship with the network density and get shorter as the density increases, for both full and partial ambiguity resolution.

scholarly journals The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones

2021 ◽  
Vol 10 (10) ◽  
pp. 699
Author(s):  
Zun Niu ◽  
Fugui Guo ◽  
Qiangqiang Shuai ◽  
Guangchen Li ◽  
Bocheng Zhu
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Smartphone Application ◽  
Satellite System ◽  
Kinematic Positioning ◽  
Multiple Sensors ◽  
Real Time Kinematic ◽  
Global Navigation Satellite ◽  
Improved Algorithm

The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub.

scholarly journals Estimation of fractional cycle bias for GPS/BDS-2/Galileo based on international GNSS monitoring and assessment system observations using the uncombined PPP model

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Jin Wang ◽  
Qin Zhang ◽  
Guanwen Huang
Keyword(s):  
Estimation Method ◽  
Satellite Orbit ◽  
Satellite System ◽  
Assessment System ◽  
Navigation Satellite ◽  
Dual Frequency ◽  
Fractional Cycle Bias ◽  
The Stability ◽  
High Consistency ◽  
Global Navigation Satellite

AbstractThe Fractional Cycle Bias (FCB) product is crucial for the Ambiguity Resolution (AR) in Precise Point Positioning (PPP). Different from the traditional method using the ionospheric-free ambiguity which is formed by the Wide Lane (WL) and Narrow Lane (NL) combinations, the uncombined PPP model is flexible and effective to generate the FCB products. This study presents the FCB estimation method based on the multi-Global Navigation Satellite System (GNSS) precise satellite orbit and clock corrections from the international GNSS Monitoring and Assessment System (iGMAS) observations using the uncombined PPP model. The dual-frequency raw ambiguities are combined by the integer coefficients (4,− 3) and (1,− 1) to directly estimate the FCBs. The details of FCB estimation are described with the Global Positioning System (GPS), BeiDou-2 Navigation Satellite System (BDS-2) and Galileo Navigation Satellite System (Galileo). For the estimated FCBs, the Root Mean Squares (RMSs) of the posterior residuals are smaller than 0.1 cycles, which indicates a high consistency for the float ambiguities. The stability of the WL FCBs series is better than 0.02 cycles for the three GNSS systems, while the STandard Deviation (STD) of the NL FCBs for BDS-2 is larger than 0.139 cycles. The combined FCBs have better stability than the raw series. With the multi-GNSS FCB products, the PPP AR for GPS/BDS-2/Galileo is demonstrated using the raw observations. For hourly static positioning results, the performance of the PPP AR with the three-system observations is improved by 42.6%, but only 13.1% for kinematic positioning results. The results indicate that precise and reliable positioning can be achieved with the PPP AR of GPS/BDS-2/Galileo, supported by multi-GNSS satellite orbit, clock, and FCB products based on iGMAS.

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 Harvester Evaluation Using Real-Time Kinematic GNSS and Hiring Service Model

2021 ◽  
Vol 3 (2) ◽  
pp. 363-382
Author(s):  
Md. Kamrul Hasan ◽  
Takashi S. T. Tanaka ◽  
Md. Rostom Ali ◽  
Chayan Kumer Saha ◽  
Md. Monjurul Alam
Keyword(s):  
Developing Countries ◽  
Real Time ◽  
Economic Performance ◽  
Net Present Value ◽  
Satellite System ◽  
Cost Ratio ◽  
Great Opportunity ◽  
Real Time Kinematic ◽  
Combine Harvesters ◽  
Asian Developing Countries

To reduce human drudgery and the risk of labor shortages in the Asian developing countries, the appropriate introduction of agricultural machinery, especially combine harvesters, is an urgent task. Custom hiring services (CHSs) are expected to contribute to making paddy harvesters prevalent in developing countries; however, the economic performance has been rarely quantified. The study was carried out to precisely evaluate the machine performance attributes of medium and large combine harvesters using the real-time kinematic (RTK) global navigation satellite system (GNSS) and to estimate the economic performance of CHSs of paddy harvesters in Japan, as a typical case of Asian countries. The financial profitability was evaluated by four major indicators: net present value, benefit–cost ratio, internal rate of return, and payback period. The financial indicators showed that both types of harvester could be considered financially viable. Thus, the investment in combine harvesters can be highly profitable for CHS business by a local service provider and custom-hire entrepreneur, providing a great opportunity to use a combine harvester without initial investment by general farmers. The findings demonstrated the high feasibility of CHSs of paddy harvesters in Japan, while they highlighted that further study is needed to estimate the feasibility of CHS in the other Asian developing countries.

APLICAÇÃO DE GEOTECNOLOGIAS NA ANÁLISE DE VIABILIDADE TÉCNICA E ECONÔMICA DO PROJETO DE IMPLANTAÇÃO DO IFMA - CAMPUS ITAQUI-BACANGA.

2021 ◽  
Vol 14 (2) ◽  
pp. 105
Author(s):  
Maelckson Bruno Barros Gomes ◽  
André Luis Silva Santos
Keyword(s):  
Real Time ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Real Time Kinematic ◽  
Global Navigation ◽  
Global Navigation Satellite

<p class="04CorpodoTexto">Este artigo tem por objetivo aplicar geotecnologias para obtenção de informações planialtimétricas a fim de avaliar a viabilidade de implantação do campus Centro Histórico/Itaqui-Bacanga do IFMA. Considerando que para realização de levantamento por métodos tradicionais é recomendado que seja realizado o destocamento e a limpeza do terreno previamente, avaliou-se a realização do levantamento planialtimétrico a partir de um par de receptores <em>Global Navigation Satellite System</em> (GNSS) pelo método <em>Real Time Kinematic</em> (RTK) pós processado e também a partir da realização de levantamento fotogramétrico, utilizando aeronave remotamente pilotada (ARP), popularmente conhecida como drone. Esta análise permitiu demonstrar que o aerolevantamento com a ARP pode ser aplicado na concepção inicial de um projeto de engenharia, conforme classificação do Tribunal de Contas da União (TCU) para níveis de precisão, pois obteve-se uma diferença orçamentária de 19% entre os projetos elaborados a partir das duas geotecnologias.</p><div> </div>

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

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