High accuracy acceleration determination by using multiple GNSS reference stations

Reliable real-time kinematic (RTK) is crucially important for emerging global navigation satellite systems (GNSSs) applications, such as drones and unmanned vehicles. The performance of conventional single baseline RTK (SBRTK) with one reference station degrades greatly in dense, urban environments, due to signal blockage and multipath error. The increasing use of multiple reference stations for kinematic positioning can improve RTK positioning accuracy and availability in urban areas. This paper proposes a new algorithm for multi-baseline RTK (MBRTK) positioning based on the equivalence principle. The advantages of the solution are to keep observation independent and increase the redundancy to estimate the unknown parameters. The equivalent double-differenced (DD) observation equations for multiple reference stations are firstly developed through the equivalent transform. A modified Kalman filter with parameter constraints is proposed, as well as a partial ambiguity resolution (PAR) strategy is developed to determine an ambiguity subset. Finally, the static and kinematic experiments are carried out to validate the proposed algorithm. The results demonstrate that, compared with single global positioning system (GPS) and Beidou navigation system (BDS) RTK positioning, the GPS/BDS positioning for MBRTK can enhance the positioning accuracy with improvement by approximately (45%, 35%, and 27%) and (12%, 6%, and 19%) in the North (N), East (E), and Up (U) components, as well as the availability with improvement by about 33% and 10%, respectively. Moreover, the MBRTK model with two and three reference receivers can significantly increase the redundancy and provide smaller ambiguity dilution of precision (ADOP) values. Compared with the scheme-one and scheme-two for SBRTK, the MBRTK with multiple reference receivers have a positioning accuracy improvement by about (9%, 0%, and 6%) and (9%, 16%, and 16%) in N, E, and U components, as well as the availability improvement by approximately 10%. Therefore, compared with the conventional SBRTK, the MBRTK can enhance the strength of the kinematic positioning model as well as improve the positioning accuracy and availability.

To evaluate the performance of real time kinematic (RTK) network algorithms without applying actual measurements, a new method called geometric precision evaluation methodology (GPEM) based on covariance analysis was presented. Three types of multiple reference station interpolation algorithms, including partial derivation algorithm (PDA), linear interpolation algorithms (LIA) and least squares condition (LSC) were discussed and analyzed. The geometric dilution of precision (GDOP) was defined to describe the influence of the network geometry on the interpolation precision, and the different GDOP expressions of above-mentioned algorithms were deduced. In order to compare geometric precision characteristics among different multiple reference station network algorithms, a simulation was conducted, and the GDOP contours of these algorithms were enumerated. Finally, to confirm the validation of GPEM, an experiment was conducted using data from Unite State Continuously Operating Reference Stations (US-CORS), and the precision performances were calculated according to the real test data and GPEM, respectively. The results show that GPEM generates very accurate estimation of the performance compared to the real data test.

Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study

By using multiple reference stations, we have developed a method to get reliable ULF global geomagnetic variations. This background is extremely useful for detecting local anomalous behaviors. This paper demonstrates developed variable tools to identify the anomalies in two frequency ranges; daily variation and 10-1000 seconds. For estimating background daily variations, the periodical model has been applied for data observed at 3 reference stations and a study station. Comparison between the first principal component of the periodical data from the reference stations and the periodical data derived from the target station provides high correlation in general. For data with 100 sec periods after wavelet filtering, the nighttime energy variations have been investigated among 3 reference stations and a study station. The similar principal component analysis as the diurnal variation has been performed and results also show high correlation between the variation at the target and the global background. These tendency suggest that the developed two methods are effective to identify the anomalies in automatic. Examining the original data, we can know details of waveform and distinguish whether the anomalies is related to underground activities or just some artificial noises.

The single-baseline solution (SBS) model has been widely adopted by the existing global navigation satellite system (GNSS) deformation monitoring systems due to its theoretical simplicity and ease of implementation. However, the SBS model neglects the mathematical correlation between baselines, and the accuracy and reliability can be degraded for baselines with long length, large height difference or frequent satellite signal occlusion. When monitoring large-area ground settlement or long-spanned linear objects such as bridges and railroads, multiple reference stations are frequently utilized, which can be exploited to improve the monitoring performance. Therefore, this paper evaluates the multi-baseline solution (MBS) model, and constrained-MBS (CMBS) model that has a prior constraint of the spatial-correlated tropospheric delay. The reliability and validity of the MBS model are verified using GPS/BDS datasets from ground settlement deformation monitoring with a baseline length of about 20 km and a height difference of about 200 m. Numerical results show that, compared with the SBS model, the MBS model can reduce the positioning standard deviation (STD) and root-mean-squared (RMS) errors by up to (47.4/51.3/66.2%) and (56.9/60.4/58.4%) in the north/east/up components, respectively. Moreover, the combined GPS/BDS positioning performance for the MBS model outperforms the GPS-only and BDS-only positioning models, with an average accuracy improvement of about 13.8 and 25.8%, with the highest accuracy improvement of about 41.6 and 43.8%, respectively. With the additional tropospheric delay constraint, the CMBS model improves the monitoring precision in the up direction by about 45.0%.

There is a trend for the establishment of regional-scale GPS permanent receiver networks, for a variety of applications including to support high accuracy, carrier phase-based positioning for surveying and precise navigation. When implemented in real-time, GPS users located within the region enclosed by multiple GPS reference stations can precisely position by using, for example, the ‘correction terms’ generated and transmitted by the reference station network. For such a configuration one of the major challenges is that the integer ambiguities have to be resolved during the real-time processing of the reference network data in order to ensure the generation of the carrier phase corrections, even when the reference receivers are many tens of kilometres apart. Due to the presence of distance dependent errors in the double-differenced data (principally the ionospheric and tropospheric delays) reliable instantaneous (single epoch) ambiguity resolution is difficult in the case of medium-scale reference networks (defined here as where the reference stations are typically in the range 50–100km apart).In practice, the ambiguities among the reference stations can be correctly resolved during an initialization procedure, but the main challenge is to continuously resolve the new ambiguities that result when the tracked satellite experiences cycle slips, or after any long data gap, or when a new satellite rises. In this paper a three-step methodology is proposed which can be implemented in realtime. Firstly, the high correlation of the atmospheric delay between adjacent epochs is used to assist cycle-slip recovery and ambiguity resolution. Then these atmospheric biases are predicted for double-differenced observations on an epoch-by-epoch and satellite-by-satellite basis. Finally these predicted atmospheric biases are applied to an algorithm that can fix the new ambiguities after a long data gap or when a new satellite rises.Data from a set of reference stations spaced 80 km apart were used to test the effectiveness of the algorithm. The results indicate that the proposed methodology can provide reliable integer ambiguities for reference stations spaced many tens of kilometres apart.

Performance evaluation of a real-time high-precision landslide displacement detection algorithm based on GNSS virtual reference station technology

The enhanced long-range navigation (eLoran) system serves as an important backup method for the global navigation satellite system (GNSS) system. In long-distance transmission scenarios, the signal propagation delay of the eLoran system is affected by fluctuations in meteorological factors along the path. Regarding these issues, such as the potential timing system errors caused by meteorological factors and the limitation on the accuracy of the timing system, in this paper, an innovative prediction model is proposed to predict the propagation delay data by fusing the propagation delay data of multiple differential reference stations on the path and the path-weighted meteorological data. By collecting and processing actual data, four types of prediction tasks were designed. Comparative analyses of the prediction performance of eight common models were conducted on a unified dataset. The results show that the Pucheng–Zhengzhou path-weighted ten-factor back-propagation neural network (PZWT-BPNN) model performs the best, achieving a balance between prediction accuracy and training efficiency. This model effectively suppresses the timing errors caused by meteorological fluctuations and improves the prediction accuracy of the propagation delay of the system, providing corresponding technical support for key fields such as low-altitude economy and transportation.

For precise positioning in arctic regions with Global Navigation Satellite Systems (GNSS), such as GPS and Glonass, there are a number of issues to be resolved or taken into account. The most important ones are satellite geometry, ionospheric effects and distribution of correction data. Precise Point Positioning (PPP) is currently the standard for precise offshore positioning. PPP requires a sparse global network of GNSS reference stations to estimate precise satellite orbit and clock parameters in real-time. These parameters are transmitted to users, who can compute their position with an accuracy of 0.1 m using code and carrier observations. The main disadvantage of PPP is that in general it takes a long time to converge to this accuracy, about 30–45 minutes. GNSS carrier observations are similar to pseudo ranges, but have an additional bias which needs to be estimated. Under certain conditions, these biases are integer. This property is used in what is generally referred to as Real-Time Kinematic (RTK) positioning, a technique which uses corrections generated at reference stations to make sure these ambiguities are integer. Integer Ambiguity Resolution (IAR) results in RTK accuracies of 0.01–0.03 m or better and short convergence times, ranging from instantaneous to several minutes for long inter-station distances. The disadvantage is the limited area of applicability, because RTK in general does not use precise orbits and clocks. Current research focuses on the combination of PPP and RTK techniques. The main goal is to achieve cm accuracy with a sparse network of reference stations. Another goal is to reduce convergence time. Applications are in the field of precise real-time offshore positioning in general, and subsidence monitoring and tidal estimation in particular. Results will be presented from standard PPP and PPP combined with IAR for arctic regions and from a test bed in the Gulf of Mexico. These results, although preliminary, indicate centimeter level accuracy is indeed possible, also in the arctic. We will also look at further improvements, mainly in convergence time, using all operational satellites and signals from current and future navigation systems.

<p>The snow water equivalent (SWE) is a key parameter in hydrology. In the past years, the signals of Global Navigation Satellite System (GNSS) receivers were discovered to be very attractive for SWE monitoring. The set-up of GNSS-based SWE monitoring typically consists of two GNSS receivers, whereas one is placed on the ground to sense the signal attenuation and time delay being caused by the snow pack. A second receiver is placed above the snow and serves as reference receiver. The measurements of both receivers are differenced to eliminate the common effect of errors in the satellite orbits and clocks, satellite phase and code biases and atmospheric errors, while the information on the snow is kept.</p><p>In this talk, we discuss the replacement of the reference receiver by a virtual reference station (VRS). The VRS is a virtual GNSS reference station, whose corrections are obtained by interpolation of the corrections from multiple surrounding reference stations to achieve a higher accuracy at the user location. The concept of VRS was first developed by Trimble and is widely used in today's real-time kinematic (RTK) positioning receivers. The concept of VRS is also attractive for snow monitoring, since the GNSS reference receiver could be avoided resulting in a lower power consumption and less costs. Moreover, this could be a big advantage for applications in slopes, which are, e.g., potentially avalanche prone. Within the hardware setup of our GNSS SWE sensors, an internet communication link for the reception of the corrections from the VRS corrections at the SWE monitoring site is already available.</p><p>However, there are also two challenges: First, the SWE monitoring stations in Alpine areas are typically at a significantly different altitude than the geodetic reference receivers. The differential tropospheric zenith delay is not negligible for altitudinal differences of more than 100 m. Therefore, the differential tropospheric delay needs to be considered either in the determination of VRS corrections or alternatively in the SWE determination. For altitudinal differences of less than 1000 m, the differential tropospheric zenith delay could be approximated by a model with sufficient accuracy. The residual modelling error is projected to the SWE estimate. Second, the use of a VRS instead of a conventional GNSS reference station requires a stronger data link, since the GNSS raw data (pseudoranges, carrier phases and carrier-to-noise power ratio measurements from all tracked satellites) need to be transmitted besides the final SWE results. However, an LTE link is totally sufficient.</p><p>Besides the methodology, we will also focus on specific hardware implementations.</p>

Though unmanned aircraft systems (UAS) are widely used in agriculture, their current positioning accuracy in a radius of 0.5 to 2 meters is still too low to pinpoint a crop row or to precisely overlay temporal multi-source field maps together without a valid geometric calibration. The positioning accuracy of UAS deployed with real time kinematic (RTK) global navigation satellite system (GNSS) can be largely increased to a centimeter level, which was claimed from the manufacturers. This paper includes the preliminary test results of positioning accuracy of a commercial RTK UAS over a set of fixed position panels in our customized scenarios. Images were collected in three GNSS modes (regular GNSS without RTK, RTK mode 1 - not corrected by the positioning error of the base station, and RTK mode 2 - corrected by the positioning error of the base station) in static and in-flight settings. In the static setting, horizontal accuracies were 2.17 cm for the RTK mode 2, 12.11 cm for the RTK mode 1, and 11.46 cm for the regular GNSS mode. The significant result of horizontal accuracy in the in-flight setting was that RTK mode 2 without GCPs (2.82 cm) showed comparable accuracy with the commonly used regular GNSS mode with GCPs (1.34 cm). The vertical positioning accuracy in the static setting were 6.01 cm for the RTK mode 2, 5.65 cm for the RTK mode 1, and 10.48 cm for the regular GNSS mode. The accuracy of height measurement from digital surface models (DSMs) without and with GCPs in RTK mode 2 were 4.81 cm and 3.72 cm, respectively, which were the best performance among the three modes. In summary, the RTK UAS tested in this study showed great potential in eliminating the requirement of using GCPs and in high-positioning-accuracy application. The next phase is to test the system in field for accurate crop height measurement at different growth stages in agricultural application.

In the Global Navigation Satellite System (GNSS) common-view (CV) time transfer system, multiple reference stations observe the same GNSS satellite at the same time to calculate the bias of the local reference time and the satellite clock. In order to get more accurate clock bias data, it is necessary to set up multiple reference terminals in each station, then integrate the results output by all terminals as the result of this station. This paper designs the multiple common-view reference terminals clock bias fusion and remote transmission software based on LabWindows/CVI. This software receives clock bias data from reference terminals, after analysis and integration, the encrypted fusion result is sent out. TCP network communication and serial communication are used for data sending and receiving, average method is adopted for data fusion temporarily, and multiple encryption technologies are used for data encryption, which ensures the security of data transmission. LabWindows/CVI provides a convenient and reliable platform for the development of this software.

In 5G networks information about localization of a user equipment (UE) can be used not only for emergency calls or location-based services, but also for the network optimization applications, e.g., network management or dynamic spectrum access by using Radio Environment Maps (REM). However, some of these applications require much better localization accuracy than currently available in 4G systems. One promising localization method is Global Navigation Satellite System (GNSS)-based Real-Time Kinematics (RTK). While the signal received from satellites is the same as in traditional GNSS, a new reception method utilizing real-time data from a nearby reference station (e.g., 5G base station) results in cm-level positioning accuracy. The aim of this paper is to obtain a model of the RTK localization error for smartphone-grade GNSS antenna under open-sky conditions, that can be used in 5G network simulators. First, a tutorial-style overview of RTK positioning, and satellite orbits prediction is provided. Next, an RTK localization simulator is implemented utilizing GNSS satellites constellations. Results are investigated statistically to provide a simple, yet accurate RTK localization error framework, which is based on two Gauss-Markov process generators parametrized by visible satellites geometry, UE motion, and UE-satellite distance error variance.

Global Navigation Satellite System (GNSS) can provide continuous and high-precision absolute displacement information and therefore has been an important technical means of landslide surface displacement monitoring. When GNSS is used for landslide monitoring, a reference station is usually set for a monitoring landslide (referred to as “one reference station for one landslide”). Through the method of short baseline relative positioning, high-precision monitoring is realized by eliminating the atmospheric propagation error with strong spatial correlation. However, this application mode has problems such as high monitoring cost, low data reuse of reference station, and heavy dependence on the single reference station for the calculation results of monitoring stations, which limit the large-scale promotion and application of GNSS in landslide monitoring. Aiming at the above problems, this study took Liujiafen Landslide in Maoxian County, Sichuan Province as a case to study the application of Continuously Operating Reference Stations (CORS) and Virtual Reference Station (VRS) issued by Natural Resource system in landslide monitoring to optimize the existing application mode of “one reference station for one landslide”. In this study, the evaluation of the quality of VRS data, the calculation accuracy of static base line, and the comparative analysis of the calculation results between virtual and physical reference stations were carried out to demonstrate the applicability of CORS in landslide monitoring. According to the results: (1) The data quality of the selected virtual reference station was good; (2) The calculation accuracy of the baseline composed of virtual reference station and monitoring station was basically equal to that of the baseline composed of physical reference station and monitoring station. In view of the regional differences in the construction density and quality of CORS reference stations, and the relationship between atmospheric propagation correction model and topographic relief, it is suggested that the application should be decided after comprehensive evaluation based on the actual distribution of reference stations and specific environmental conditions.

Global Navigation Satellite Systems (GNSS) are used to provide accurate position, navigation, and time (PNT) information to users in various sectors of our society including transportation. Augmentation systems such as differential GNSS (DGNSS), real-time kinematics (RTK), and Precise Point Positioning (PPP) improve the GNSS performance, and providing reliable measurements from its reference station is very crucial. To ensure safe and accurate PNT solutions, code and carrier measurements must be monitored for potential faults or a performance degrade. Although there exist numerous methods to model and monitor the measurements, research on the carrier phase measurements is not as extensive as the code measurements. This paper introduces a split of residuals into receiver noise and multipath components to customize their estimation according to their respective statistical properties. This study also proposes a method to use machine learning-based non-linear regression to effectively model and monitor potential faults in the GNSS measurements including the carrier phase. A training dataset is used to model the nominal quantities of GNSS measurement residuals, and inflation factors are applied to over-bound the fault-free residuals. These inflated residuals are coupled with uncertainty factors to compute thresholds for monitoring carrier phase residuals, and the effectiveness of the thresholds is validated with a test dataset by achieving the false alarm rate of 6.61×10−6, slightly lower than the desired level of 10−5.