Global Navigation Satellite System Positioning Research Articles

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

Evaluating Optical Clock Performance for GNSS Positioning.

Atomic clocks are highly precise timing devices used in numerous Positioning, Navigation, and Timing (PNT) applications on the ground and in outer space. In recent years, however, more precise timing solutions based on optical technology have been introduced as current technology capabilities advance. State-of-the-art optical clocks-predicted to be the next level of their predecessor atomic clocks-have achieved ultimate uncertainty of 1 × 10-18 and beyond, which exceeds the best atomic clock's performance by two orders of magnitude. Hence, the successful development of optical clocks has drawn significant attention in academia and industry to exploit many more opportunities. This paper first provides an overview of the emerging optical clock technology, its current development, and characteristics, followed by a clock stability analysis of some of the successfully developed optical clocks against current Global Navigation Satellite System (GNSS) satellite clocks to discuss the optical clock potentiality in GNSS positioning. The overlapping Allan Deviation (ADEV) method is applied to estimate the satellite clock stability from International GNSS Service (IGS) clock products, whereas the optical clock details are sourced from the existing literature. The findings are (a) the optical clocks are more stable than that of atomic clocks onboard GNSS satellites, though they may require further technological maturity to meet spacecraft payload requirements, and (b) in GNSS positioning, optical clocks could potentially offer less than a 1 mm range error (clock-related) in 30 s and at least 10 times better timing performance after 900 s in contrast to the Galileo satellite atomic clocks-which is determined in this study as the most stable GNSS atomic clock type used in satellite positioning.

ThingSpeak an IOT Application and Analytics System for GNSS with MATLAB Analysis

The ionosphere is an essential component of the Global Navigation Satellite System (GNSS) that can affect the accuracy and dependability of GNSS location. MATLAB provides comprehensive tools for the analysis of ionospheric data and its impact on GNSS positioning. To examine the ionospheric data utilized in GNSS positioning, we advise utilizing MATLAB in this application. The analysis is based on a determination of the Total Electron Content (TEC) of the ionosphere in order to account for the ionospheric delay in the GNSS signals. The system consists of a GNSS receiver module connected to a microcontroller with Wi-Fi capabilities, which transmits data to Matlab. The data is processed in Matlab before being used there. Then, in order to account for the ionospheric delay in the GNSS signals, the TEC of the ionosphere is calculated using Matlab. The recommended system provides a scalable and flexible environment for GNSS-based ionospheric data analysis. The system is practical for a range of applications, including surveying, geodesy, and navigation, and it may be readily adapted to fulfill specific customer requirements.For Matlab analysis, the approach has a number of advantages. In order to measure the TEC of the ionosphere precisely, it first provides cutting-edge signal processing technologies. Additionally, it makes it possible to display ionospheric data in a number of different ways, such as maps, graphs, and time-series plots. This can be applied to the data to identify patterns and trends. The proposed MATLAB-based system offers a scalable, flexible, and cutting-edge platform for the estimation of the ionosphere's TEC and the correction of the ionospheric delay in the GNSS signals.

Image Mapping Accuracy Evaluation Using UAV with Standalone, Differential (RTK), and PPP GNSS Positioning Techniques in an Abandoned Mine Site.

Global navigation satellite systems (GNSSs) provide a common positioning method that utilizes satellite signals to determine the spatial location of a receiver. However, there are several error factors in standalone GNSS positioning due to instrumental, procedural, and environmental factors that arise during the signal transmission process, and the final positioning error can be up to several meters or greater in length. Thus, real-time kinematic (RTK) correction and post-mission precise point positioning (PPP) processing technologies are proposed to improve accuracy and accomplish precise position measurements. To evaluate the geolocation accuracy of mosaicked UAV images of an abandoned mine site, we compared each orthomosaic image and digital elevation model obtained using standalone GNSS positioning, differential (RTK) GNSS positioning, and post-mission PPP processing techniques. In the three types of error evaluation measure (i.e., relative camera location error, ground control points-based absolute image mapping error, and volumetric difference of mine tailings), we found that the RTK GNSS positioning method obtained the best performance in terms of the relative camera location error and the absolute image mapping error evaluations, and the PPP post-processing correction effectively reduced the error (69.5% of the average total relative camera location error and 59.3% of the average total absolute image mapping error) relative to the standalone GNSS positioning method. Although differential (RTK) GNSS positioning is widely used in positioning applications that require very high accuracy, post-mission PPP processing can also be used in various fields in which it is either not feasible to operate expensive equipment to receive RTK GNSS signals or network RTK services are unavailable.

Accuracy of GNSS Position Stored in Fishing Boat Location Transmitters in Comparison with That of DGPS Position

Fishing boat laws in Korea require fishing boats to be equipped with a location transmitter. Approximately 91% of registered fishing boats have V-pass terminals, whereas those with a gross tonnage of 10 t or more have either V-pass or automatic identification system (AIS) terminals (or use both). Most navigators, including the fishing boat location transmitter, rely on the satellite signals provided by the global navigation satellite system (GNSS) without considering the accuracy of the navigation instruments that indicate ship location. Many scholars are conducting research to analyze the accuracy of GNSS locations through continuous experiments; however, few of them focus on fishing boat location transmitters. Particularly, the location accuracy of V-pass equipment is unknown. Notably, the V-pass and AIS terminals used in fishing boats are mainly designed to locate fishing boats in need of assistance following a marine accident rather than to provide information on approaching ships and preventing collisions. Therefore, this study aimed to compare GNSS location storage data extracted from the location transmitter (V-pass and AIS terminal) of the accident fishing boat with the DGPS location information from the Electronic Chart Display and Information System to check the position error and use it for accident analysis and investigation.

Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning

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.

A method for separating unmodeled errors in GNSS based on the constraint of the prior variance of unit weight

In global navigation satellite system (GNSS), unmodeled errors critically affect the accuracy and reliability of positioning solutions. When the unmodeled errors are significant in the mathematical model, they are mainly processed by choosing adjustment models with additional systematic error parameters or semiparametric estimation. However, many existing methods require knowledge of prior information on unmodeled errors; otherwise, achieving better processing results is difficult. To address this problem, this study proposes a GNSS unmodeled error separation method that does not rely on prior information on unmodeled errors. This method is based on the constraint of prior variance of unit weight. First, the method effectively separates the effect of unmodeled errors in the residuals under this constraint. Second, the initial estimate of the unmodeled errors in the observation domain is used as a virtual observation. Thus, the optimal estimate and variance of the unmodeled errors are obtained. Finally, the observations are effectively corrected by combining the concepts of mean shift and variance inflation. Multiple experiments were performed in this study. The results show that the proposed method can effectively weaken the impact of unmodeled errors on the float and fixed solutions of GNSS positioning, regardless of whether unmodeled errors exist in a single observation or multiple observations.

Instantaneous GNSS Ambiguity Resolution and Attitude Determination via Riemannian Manifold Optimization

We consider the problem of Global Navigation Satellite System (GNSS)-based attitude determination. A GNSS attitude model with nonlinear orthonormality constraints is used to rigorously incorporate the a-priori knowledge of the receiver geometry. Given the characteristics of the employed nonlinear constraints, we formulate GNSS attitude determination as an optimization problem on a Riemannian manifold. We design a Riemannian algorithm to deliver the constrained float attitude matrix solution. Subsequently, the constrained float solution, combined with a proposed decomposition of the objective function, is utilized to enhance the efficiency of the integer ambiguity search. Both simulation and experimental evidences demonstrate the superiority of the proposed method. The results reveal that the proposed method can maintain the high probability of resolving the integer ambiguities correctly while enjoying the low computational complexity compared with the state-of-the-art techniques.

Investigating the Effects of Ionospheric Scintillation on Multi‐Frequency BDS‐2/BDS‐3 Signals at Low Latitudes

AbstractIonospheric scintillation could seriously disrupt the signal tracking of the global navigation satellite systems (GNSS), further causing positioning accuracy degradation or unavailability. BeiDou navigation satellite system (BDS), a newly developed GNSS by China, has begun to provide global positioning, navigation, and timing service. The objective of the present study is to investigate the effects of ionospheric scintillation on BDS‐2 and BDS‐3 multi‐frequency signals. Ionospheric scintillation monitor receiver data from four monitors in Brazil were collected from October 2021 to May 2022. The results illustrate that S4(B2) and S4(B3) linearly increase with S4(B1) for S4(B1) ≤ 0.6, which is consistent with weak scattering theories, and average experimental ratios of S4(B2)/S4(B1), S4(B3)/S4(B1), and S4(B2)/S4(B3) are less than corresponding theoretical ones by 6.1%, 4.4%, and 1.9%, respectively. Meanwhile, as S4 values increase, lower‐frequency scintillation saturates earlier than higher ones, and the probability of ionospheric scintillation events on B2 and B3 signals is approximately twice (S4 ≥ 0.7) as B1 signals in the equatorial ionization anomaly (EIA) regions. To alleviate the undesirable effects of missing data on GNSS positioning, we first investigate the inter‐frequency relationship and distribution probability of two significant spectral parameters, that is, T (the spectral strength of the phase noise at 1 Hz) and p (the spectral slope of the phase power spectral density) in the tracking jitter model among three BDS frequencies. Results show that the performances among B1, B2, and B3 frequencies have a higher correlation respectively, and their values for B2 and B3 signals are more susceptible to be impacted by ionospheric scintillation.

Assessment of GNSS zenith tropospheric delay responses to atmospheric variables derived from ERA5 data over Nigeria

Tropospheric delay is a major error caused by atmospheric refraction in Global Navigation Satellite System (GNSS) positioning. The study evaluates the potential of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5) atmospheric variables in estimating the Zenith Tropospheric Delay (ZTD). Linear regression models (LRM) are applied to estimate ZTD with the ERA5 atmospheric variables. The ZTD are also estimated using standard ZTD models based on ERA5 and Global Pressure and Temperature 3 (GPT3) atmospheric variables. These ZTD estimates are evaluated using the data collected from the permanent GNSS continuously operating reference stations in the Nigerian region. The results reveal that the Zenith Hydrostatic Delay (ZHD) from the LRM and the Saastamoinien model using ERA5 surface pressure are of identical accuracy, having a Root Mean Square (RMS) error of 2.3 mm while the GPT3-ZHD has an RMS of 3.4 mm. For the Zenith Wet Delay (ZWD) component, the best estimates are derived using ERA5 Precipitable Water Vapour (PWV). These include the ZWD derived by the LRM having an average RMS of 20.9 mm and Bevis equation having RMS of 21.1 mm and 21.0 mm for global and local weighted mean temperatures, respectively. The evaluation of GPT3-ZWD estimates gives RMS of 45.8 mm. This study has provided a valuable insight into the application of ERA5 data for ZTD estimation. In line with the findings of the study, the ERA5 atmospheric variables are recommended for improving the accuracy in ZTD estimation, required for GNSS positioning.

Editorial for Special Issue “Precise GNSS Positioning and Navigation: Methods, Challenges, and Applications”

The Global Navigation Satellite System (GNSS) can provide users with high-precision positioning information continuously and benefits all walks of life, e [...]

Characterisation of a new lightweight LoRaWAN GPS bio-logger and deployment on griffon vultures Gyps fulvus

1. Information provided by tracking studies using remote telemetry is providing ecologists with invaluable new insights into animal behaviour and movement strategies. Here we describe a new type of GNSS (Global Navigation Satellite System) tracking device currently under development and nearing commercialisation, which transmits data via LoRaWAN (long range wide area network) gateways. These tags have the potential to be a low weight and power consumption solution for tracking the movement of animals at high resolution. 2. We characterise the position accuracy and data transmission range, including uplinks and downlinks, for the tracker using a series of ground-based field tests. Data transmission range was tested by visiting locations with line of sight to the LoRaWAN Gateway at distances up to 75 km and recording whether data transmission was completed successfully from each location. These tests were complemented by a trial deployment of six devices on griffon vultures Gyps fulvus. 3. These LoRa tags reliably provided accurate position estimates, particularly on more frequent acquisition cycles. At 1-min intervals the GNSS location bias was 4.71 m in the horizontal plane and 5 m in the vertical plane while precision, measured by standard deviation, was 3.9 m in horizontal space and 7.7 m in vertical space. Ground-based range tests confirmed data transmission from a maximum distance of 40.7 km. Initial results from a deployment on griffon vultures yielded useful information about flight speeds, altitude, and transmission range (up to 53.4 km). 4. With consistent GNSS position accuracy and the ability to transmit data over tens of kilometres, the LoRa tags demonstrated potential for monitoring animal movement over large areas. The small size and power needs of the device allow for flexibility in which combination of battery, solar panel, and housing they are paired with. The tags can be assembled in housing formats ranging in size from less than 5 g for deployment on Kestrel sized birds to 80 g for deployment on large birds such as vultures. The devices are particularly suitable for philopatric (site-faithful) species because LoRa gateways can be installed near breeding sites to maximise opportunities for data transmission. Our findings are informative for studies seeking to use LoRa for tracking birds and other animals using the miro-Nomad or a different type of GPS-LoRa logger.

Accuracy Enhancement and Feature Extraction for GNSS Daily Time Series Using Adaptive CEEMD-Multi-PCA-Based Filter

Global navigation satellite system (GNSS) positions include various useful signals and some unmodeled errors. In order to enhance the accuracy and extract the features of the GNSS daily time sequence, an improved method of complete ensemble empirical mode decomposition (CEEMD) and multi-PCA (MPCA) based on correlation coefficients and block spatial filtering was proposed. The results showed that the mean standard deviations of the raw residual time sequence were 1.09, 1.20 and 4.79 mm, while those of the newly proposed method were 0.15, 0.20 and 2.86 mm in north, east and up directions, respectively. The proposed method outperforms wavelet decomposition (WD)-PCA and empirical mode decomposition (EMD)-PCA in effectively eliminating low- and high-frequency noise, and is suitable for denoising nonlinear and nonstationary GNSS position sequences. Furthermore, feature extraction of the denoised GNSS daily time series was based on CEEMD, which is superior to WD and EMD. Results of noise analysis suggested that the noise components in the original and denoised GNSS time sequence are complex. The advantages of the proposed method are the following: (i) it fully exploits the merits of CEEMD and WD, where CEEMD is first used to obtain the limited intrinsic modal functions (IMFs) and then to extract seasonal and trend features; (ii) it has good adaptive processing ability via WD for noise-dominant IMFs; and (iii) it fully considers the correlation between the different components of each station and the non-uniform behavior of common mode error on a spatial scale.

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors

Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone's ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

Underwater Positioning System Based on Drifting Buoys and Acoustic Modems

GNSS (Global Navigation Satellite System) positioning is not available underwater due to the very short range of electromagnetic waves in the sea water medium. In this article a LBL (Long Base Line) acoustic repeater system of the GNSS positioning is presented. The system is hyperbolic, i.e., based on time differences and it does not need very accurate atomic clocks to synchronize repeaters. The system architecture and system calculations that demonstrate the feasibility of the solution are presented. The system uses four buoys that sequentially transmit their position and the time of the instant of transmission, for which they are equipped with GNSS receivers and acoustic modems. The buoys can be fixed or even drifting, but they are inexpensive devices, which pose no hazard to navigation and can be easily and quickly deployed for a specific underwater mission. The multilateration algorithm used in the receiver is presented. To simplify the algorithm, the depth of the receiver, measured by a depth sensor, is used. Results are presented for the position error of an underwater vehicle due to its displacement during the transmission frame of the four buoys.

Impact of Additional Antenna Groundplanes on RTK-GNSS Positioning Accuracy of UAVs

Abstract. Precise position information is important for terrestrial and airborne surveying systems, such as unmanned aerial vehicles (UAVs). Those systems often rely on real-time kinematic (RTK) global navigation satellite systems (GNSSs) for position determination, where the GNSS antenna mounting environment impacts the GNSS position accuracy to a great extent. This paper investigates the impact of different supplementary groundplane shapes, sizes, and materials on multi-band patch and helical GNSS antennas at both, the UAV rover and RTK base station with respect to the achievable position accuracy. The groundplanes consist of solid aluminum sheets or copper plated printed circuit boards (PCBs) and are mounted directly underneath the GNSS antennas. Appropriate supplementary groundplanes are found to significantly improve the GNSS position accuracy in the majority of test cases.

Incorporation of Real-Time Earthquake Magnitudes Estimated via Peak Ground Displacement Scaling in the ShakeAlert Earthquake Early Warning System

ABSTRACTThe United States earthquake early warning (EEW) system, ShakeAlert®, currently employs two algorithms based on seismic data alone to characterize the earthquake source, reporting the weighted average of their magnitude estimates. Nonsaturating magnitude estimates derived in real time from Global Navigation Satellite System (GNSS) data using peak ground displacement (PGD) scaling relationships offer complementary information with the potential to improve EEW reliability for large earthquakes. We have adapted a method that estimates magnitude from PGD (Crowell et al., 2016) for possible production use by ShakeAlert. To evaluate the potential contribution of the modified algorithm, we installed it on the ShakeAlert development system for real-time operation and for retrospective analyses using a suite of GNSS data that we compiled. Because of the colored noise structure of typical real-time GNSS positions, observed PGD values drift over time periods relevant to EEW. To mitigate this effect, we implemented logic within the modified algorithm to control when it issues initial and updated PGD-derived magnitude estimates (MPGD), and to quantify MPGD uncertainty for use in combining it with estimates from other ShakeAlert algorithms running in parallel. Our analysis suggests that, with these strategies, spuriously large MPGD will seldom be incorporated in ShakeAlert’s magnitude estimate. Retrospective analysis of data from moderate-to-great earthquakes demonstrates that the modified algorithm can contribute to better magnitude estimates for Mw>7.0 events. GNSS station distribution throughout the ShakeAlert region limits how soon the modified algorithm can begin estimating magnitude in some locations. Furthermore, both the station density and the GNSS noise levels limit the minimum magnitude for which the modified algorithm is likely to contribute to the weighted average. This might be addressed by alternative GNSS processing strategies that reduce noise.

The Generalized Method of Wavelet Moments with eXogenous inputs: a fast approach for the analysis of GNSS position time series

The global navigation satellite system (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow to study global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work, we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time-series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogenous variables: it is semi-parametric, computationally efficient and scalable. Unlike standard methods such as the widely used maximum likelihood estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results (theoretical and obtained in simulations) show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach have important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations (some of them being recorded over several decades) quickly becomes computationally prohibitive. Compared to standard likelihood-based methods, the GMWMX has a considerably reduced algorithmic complexity of order mathcal {O}{log (n) n} for a time series of length n. Thus, the GMWMX appears to provide a reduction in processing time of a factor of 10–1000 compared to likelihood-based methods depending on the considered stochastic model, the length of the time series and the amount of missing data. As a consequence, the proposed method allows the estimation of large-scale problems within minutes on a standard computer. We validate the performances of our method via Monte Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power law or Matérn processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA.

Improving GNSS positioning performance of Android smart devices by a novel pseudorange correction method

To achieve high-precision global navigation satellite system (GNSS) positioning of smart devices, it is necessary to correct the pseudorange measurements, especially to reduce time-related errors. Traditional pseudorange smoothing methods fail to cope with measurement anomalies and do not consider the time correlation. Therefore, a time-correlated pseudorange correction (TCPC) method is proposed. The Mann-Kendall trend test and breakpoint detection are used to address data anomalies and pseudorange errors are estimated with an adaptive smoothing method. Using the proposed method, the GNSS receivers can improve the pseudorange accuracy and availability for better single-point positioning performance. The positioning errors are reduced by 10%–45% for different GNSS receivers. Moreover, the circular antenna motion mechanism is used to reduce the measurement correlation and enhance observability. Using the TCPC method in the smartphone antenna motion case, the improvement rates of positioning accuracies are 43%–76% and the horizontal positioning accuracy is improved from meter level to decimeter level.

Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna

One of the technologies used for localization is GNSS (Global Navigation Satellite Systems), which is exposed to many phenomena, i.e., the occurrence of terrain covers, reflections of the radio waves, and the multipath nature of the radio waves in the propagation environment. To increase the resistance to environmental phenomena, special types of antennas are used, which include, among others, choke ring antennas. The article describes the design and construction of the choke ring antenna and the impact of the mentioned device on the quality of GNSS positioning. The accuracy of the constructed antenna (based on selected accuracy measures: DRMS, 2DRMS, SEP, MRSE, SAE) is calculated together with positioning errors determined for two types of antennas: choke ring antenna and the dedicated antenna supplied by the manufacturer in RTK mode. The results confirm that the designed choke ring antenna can be used for positioning without significantly losing accuracy in the RTK mode.