Global Variation of ULF Geomagnetic Fields and Detection of Anomalous Changes at a Certain Observatory using Reference Data

High accuracy acceleration determination by using multiple GNSS reference stations

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

BackgroundThe Multi-Biomarker Disease Activity (MBDA) score has been validated as a disease activity metric in rheumatoid arthritis (RA) patients. Patients initiating new therapy or changing therapy frequently have moderate to...

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.

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.

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.

Meteorological records, including precipitation, commonly have missing values. Accurate imputation of missing precipitation values is challenging, however, because precipitation exhibits a high degree of spatial and temporal variability. Data-driven spatial interpolation of meteorological records is an increasingly popular approach in which missing values at a target station are imputed using synchronous data from reference stations. The success of spatial interpolation depends on whether precipitation records at the target station are strongly correlated with precipitation records at reference stations. However, the need for reference stations to have complete datasets implies that stations with incomplete records, even though strongly correlated with the target station, are excluded. To address this limitation, we develop a new sequential imputation algorithm for imputing missing values in spatio-temporal daily precipitation records. We demonstrate the benefits of sequential imputation by incorporating it within a spatial interpolation based on a Random Forest technique. Results show that for reliable imputation, having a few strongly correlated references is more effective than having a larger number of weakly correlated references. Further, we observe that sequential imputation becomes more beneficial as the number of stations with incomplete records increases. Overall, we present a new approach for imputing missing precipitation data which may also apply to other meteorological variables.

Ozone vertical profile in the troposphere was continually measured by lidar at Tsukuba, Japan and diurnal variations of ozone was investigated in relation to the daily weather system variations. A chemistry-climate model including detailed tropospheric chemistry was also run to get continuous ozone distribution. Based on the observed and simulated ozone profile in the troposphere, diurnal and daily variations in the actinic flux of ultraviolet radiation at the surface were calculated under a clear sky assumption. A preliminary calculation shows that the reduction in UVB due to tropospheric ozone decreases in magnitude with wavelengths, while the reduction displays greater diurnal variations at middle wavelengths (290 and 300 nm) than the shorter (280 nm) and the longer (310 nm) wavelengths.

A network of multiple ground-based, continuous GPS reference stations for generating carrierphase- based corrections can provide a higher achievable position accuracy, and increase the speed and effectiveness of carrier phase integer ambiguity resolution for precise real-time positioning in relation to the traditional single baseline approach. This method also allows for a lower number of reference stations required to cover a larger service area than the traditional single reference station approach. The focus of this paper is to investigate the multiple reference station method for generating carrier- phase-based corrections using the Marmara Continuous GPS Network, Turkey. The carrier phase measurement corrections for the coverage area are computed using the NetAdjust method, developed at the University of Calgary. Results and analysis using the regional carrier-phase corrections are compared to the single reference receiver approach to demonstrate the achievable improvement with this method The results show 55 to 75 percent improvement in the observation domain, 40 to 60 percent improvement in the real-time position accuracy, and an 11 to 38 percent decrease in the time required to resolve L1 ambiguities. The corresponding position accuracies for a user's receiver show as much as 58, 45, and 35 percent improvement in the latitude, longitude and height, respectively, over the single baseline approach.

Virtual Reference Station (VRS) is a kind of network RTK technology. For solving the limitations of real-time standard dynamic (RTK) system, a new method for high-precision RTK positioning VRS is proposed. At the same time, aiming at the real-time solution of the ambiguity of VRS network, a new method based on the non-ionospheric combination equation to solve the ambiguity is proposed. The linear interpolation model is used to generate the comprehensive error correction number, and the virtual observation value is proposed. The method was generated and tested in Nanchang University and Poyang Lake area. The test was conducted at different locations around Poyang Lake to assess the accuracy of VRS RTK positioning; the results show that the use of multiple reference station networks increases the coverage of location service areas and enhances robustness over a single reference station system, got higher positioning accuracy. The VRS RTK is positioned in the experimental network with a horizontal accuracy of 1–2 cm and a vertical accuracy of 3–5 cm.

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%.

Abstract. In order to extract any ULF signature associated with earthquakes, the principal component analysis (PCA) and singular spectral analysis (SSA) have been performed to investigate the possibility of discrimination of signals from different sources (geomagnetic variation, artificial noise, and the other sources (earthquake-related ULF emissions)). We adopt PCA to the time series data observed at closely separated stations, Seikoshi (SKS), Mochikoshi (MCK), and Kamo (KAM). In order to remove the most intense signal like the first principal component, we make the differential data sets of filtered 0.01Hz SKS-KAM and MCK-KAM in NS component and 0.01 Hz band. The major findings are as follows. (1) It is important to apply principal component analysis and singular spectral analysis simultaneously. SSA gives the structure of signals and the number of sensors for PCA is estimated. This makes the results convincing. (2) There is a significant advantage using PCA with differential data sets of filtered (0.01 Hz band) SKS-KAM and MCK-KAM in NS component for removing the most intense signal like global variation (solar-terrestrial interaction). This provides that the anomalous changes in the second principal component appeared more sharply. And the contribution of the second principal component is 20–40%. It is large enough to prove mathematical accuracy of the signal. Further application is required to accumulate events. These facts demonstrate the possibility of monitoring the crustal activity by using the PCA and SSA.

The most commonly used multivariate analysis (MVA) techniques in food science, that is, principal component, discriminant and cluster analyses and various combinations of these are compared using nonfood related and food protein structure-function data. Although discriminant analysis (DA) is the most efficient and useful method for classifying samples into groups, there is a risk of discarding useful information, since DA uses only that portion of data variation that is useful for the classification purpose. Therefore, DA is recommended only when the groups are clearly defined a priori. If one wishes to group continuous data, for example native versus denatured protein without a clear distinction between groups, principal component regression is recommended. Principal component-similarity analysis (PCS) comprises a group of sequentially applied MVA techniques proposed for classification of samples when the grouping cannot be clearly defined in advance or when only a few samples are available for classification. PCS classifies samples with minimal loss of data variation.

A probabilistic model for the prediction of meteorological droughts in Venezuela

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.