GPS Solutions

Abstract

The application field of global navigation satellite systems continues to expand, and their security and stability have received widespread attention. Navigation spoofing has the characteristics of solid concealment and significant harm, posing a severe security threat to navigation systems. In current spoofing detection methods based on signal spatial correlation, multiple antennas/receivers or moving single antennas are required, which means high cost and complexity in implementation. To this end, we propose a spoofing detection method based on the intersection angle between two directions of arrival (IA-DOA) for single-antenna receivers. The essence of this method is to accurately estimate the IA-DOA between a pair of signals based on pseudorange observations and navigation information. The observation should be consistent with the prediction when there is no spoofing. Otherwise, due to geometric and kinematic differences between the navigation satellite and the spoofer or the pulling off of the spoofing, the spoofing may disrupt the consistency between the observation and prediction of IA-DOA. Theoretically, since the proposed method makes no assumptions about spoofing, it can detect multi-antenna spoofing. We conducted a Monte Carlo simulation to analyze the impact of different parameters on spoofing detection performance and conducted experimental verification and evaluation through open datasets. The results show that the method proposed in this article can effectively detect multi-antenna spoofing, reducing the requirements of receiver antennas for spoofing detection methods based on signal spatial correlation.

Abstract

In January 2023, the International Laser Ranging Service (ILRS) approved the tracking of 20 additional BeiDou-3 Medium Earth Orbit (BDS-3 MEO) satellites, integrating them into the ILRS tracking network. Before that, only 4 BDS-3 MEO satellites had been tracked. BDS satellites employ highly advanced GNSS components and technological solutions; however, microwave-based orbits still contain systematic errors. Satellite Laser Ranging (SLR) tracking is thus crucial for better identification and understanding of orbit modeling issues. Orbit improvements are necessary to consider BDS in future realizations of terrestrial reference frames, supporting the determination of global geodetic parameters and utilizing them for the co-location of GNSS and SLR in space. In this study, we summarize the first 6 months of SLR tracking 24 BDS-3 MEO satellites. The study indicates that the ILRS network effectively executed the request to track the entire BDS-3 MEO constellation. The number of observations is approximately 1300 and 450 for high- and low-priority BDS-3 satellites, respectively, over the 6 months. More than half of the SLR observations to BDS-3 MEO satellites were provided by 5 out of the 24 laser stations, which actively measured GNSS targets. For 14 out of 24 BDS-3 MEO satellites, the standard deviation of SLR residuals is at the level of 19–20 mm, which is comparable with the quality of the state-of-the-art Galileo orbit solutions. However, the SLR validation of the individual satellites revealed that the BDS-3 MEO constellation consists of more ambiguous groups of satellites than originally reported in the official metadata files distributed by the BDS operators. For 8 BDS-3 satellites, the quality of the orbits is noticeably inferior with a standard deviation of SLR residuals above 100 mm. Therefore, improving orbit modeling for BDS-3 MEO satellites remains an urgent challenge for the GNSS community.

Abstract

In recent years, the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) for monitoring sea-level variations has been explored extensively. However, most studies have typically selected coastal sites with optimal GNSS-IR observation conditions, often limiting verification to one or two stations and overlooking other viable sites. This study introduces a site-evaluation strategy named facilitated interferometric reflectometry evaluation (FIRE), which utilizes two constructed indices—normalized available observation time and normalized sampling deficiency—to assess the suitability of GNSS sites for sea-level retrieval. Implemented in Hong Kong, China, the strategy was applied to all 19 sites within the local reference GNSS network. In addition to the two sites previously used, we identified five additional sites conducive to tidal GNSS-IR, demonstrating precisions between 0.07 m and 0.37 m and correlations ranging from 0.986 to 0.711. With denser GNSS-IR observations, we were able to map sea-level variations during three historic typhoon storm surges in the region more precisely: 2017 Typhoon Hato, 2018 Typhoon Mangkhut, and 2023 Typhoon Saola. Typhoon Saola presented a longer duration of six days and a slightly lower sea-level peak between 2.88 m and 3.13 m. The deployment of additional tidal GNSS-IR sites revealed variations in sea levels during typhoon storm surges, with differences influenced by coastline topography of up to 1.24 m observed during Typhoon Mangkhut. These sites offer a valuable supplement to traditional tide measurements, filling gaps in historical records. The FIRE strategy demonstrates the untapped potential of existing GNSS networks globally for sea-level monitoring and can be employed to unlock further observational opportunities.

Abstract

High-accuracy GNSS positioning in urban environments is important for applications like safe autonomous driving, however, dynamic errors in complex urban environments limit positioning performances. Recently, deep learning-based (DL) approaches can obtain better GNSS positioning solutions in complex urban environments than model-based ones. However, DL-based approaches simply concentrate one-view GNSS observations as inputs, which are insufficient to model vehicle states accurately, and temporally continuous observations are highly correlated, leading to inaccurate positioning correction results. To solve the challenge, we propose a Sparse Representation-based Multiview Deep Reinforcement Learning model for positioning correction, which employs attention-based multiview fusion to process multiview observations, and uses sparse representation to alleviate disturbances from highly correlated observations. To represent the vehicle state sufficiently, we build a multiview positioning correction environment, and develop an attention-weighted multiview fusion module to fuse temporal features as belief states based on adaptively learned attention weights. To effectively process redundant and correlated multiview features, we impose the ℓ1 norm regularizer to learn sparse hidden representations and improve the precision of value estimation. Finally, we construct a sparse representation-driven multiview actor-critic positioning correction model to achieve high-accuracy GNSS positioning in complex urban environments. We validate performances in both Google Smartphone Decimeter Challenge (GSDC) datasets and our collected GNSS datasets in the Guangzhou area (GZGNSS). Experimental results show that our algorithm can improve localization performances with 27% improvements from WLS+KF in GSDC trajectories, 16% from RTK, and 6% from DL-based methods in GZGNSS trajectories.

Abstract

Troposphere augmentation is of great importance for global navigation satellite system (GNSS) real-time precise point positioning (PPP) service. This contribution focuses on the feasibility of modeling the regional troposphere by polynomial fitting and the benefits of precise tropospheric corrections for triple-frequency and multi-GNSS PPP and PPP with integer ambiguity resolution (PPP-IAR) during a period of typhoon weather. A modified optimal fitting coefficient (MOFC) method is proposed with the height-related parameters removed by a priori fitted exponential function. Two spatial scales of networks are chosen to verify the effect of the GNSS station distribution on troposphere modeling. The results show that the MOFC model can provide centimeter-level accuracy with average root mean square (RMS) of 2.1 and 2.2 cm for dense and sparse networks, respectively, while that of GPT2w and real-time VMF3-FC products are 6.6 and 3.3 cm during typhoon periods. PPP/PPP-IAR tests with zenith troposphere delay (ZTD) augmentation based on the MOFC model are conducted when a typhoon eye passes over. Accuracy improvements of 18.2 and 16.6% for vertical components are observed in BDS-only and BDS/Galileo/GPS PPP-IAR solutions with ZTD augmentation, while those for PPP float solutions are marginal. Additionally, 2-h positioning arcs for PPP float solutions and 1224 10-min arcs for PPP-IAR solutions confirm that ZTD augmentation plays an important role in convergence, especially for PPP-IAR solutions. The percentage of instantaneous convergence in BDS-only PPP-IAR solutions improves from 42.1, 44.0 and 18.9% to 51.3, 52.3 and 48.9% for the east, north and up components, respectively, indicating that decorrelation between ZTD and vertical coordinates can be achieved with MOFC ZTD corrections in the initial stage of positioning. The percentages further improved from 89.7, 89.5 and 74.6% to 94.1, 94.2 and 93.7% for BDS/Galileo/GPS PPP-IAR solutions.

Abstract

Global navigation satellite system (GNSS) data collected from Android devices have gained increasing importance in various applications, ranging from geospatial positioning to environmental monitoring. However, the lack of standardized tools for converting Android GNSS raw data into receiver independent exchange (RINEX) format poses a significant challenge for researchers and practitioners. In response to this need, we present a comprehensive Python toolbox designed to streamline the conversion process and enhance the usability of Android GNSS data. The proposed toolbox leverages Python’s versatility to provide a user-friendly interface for converting Android GNSS raw data into the widely adopted RINEX format. Key features include robust data parsing algorithms, support for multiple GNSS constellations, and compatibility with diverse Android device configurations. Furthermore, the toolbox’s open-source nature encourages community collaboration and allows for continual improvement and adaptation to emerging GNSS technologies. We anticipate that this Python toolbox will serve as a valuable resource for researchers and practitioners working with Android GNSS data, facilitating standardized data interchange and promoting reproducibility in GNSS-based studies.

Abstract

The Galileo high accuracy service (HAS) is a new capability of the European global navigation satellite system, currently providing satellite orbit and clock corrections and dispersive effects such as satellite instrumental biases for code and phase. In its full capability, Galileo HAS will also correct the ionospheric delay on a continental scale (initially over Europe). We analyze a real-time ionospheric correction system based on the fast precise point positioning (F-PPP), and its potential application to the Galileo HAS. The F-PPP ionospheric model is assessed through a 281-day campaign, confirming previously reported results, where the proof of concept was introduced. We introduce a novel real-time test that directly links the instantaneous position error with the error of the ionospheric corrections, a key point for a HAS. The test involved 15 GNSS receivers in Europe acting as user receivers at various latitudes, with distances to the nearest reference receivers ranging from tens to four hundred kilometers. In the position domain, the test results show that the 95th percentile of the instantaneous position error depends on the user-receiver distance, as expected, ranging in the horizontal and vertical components from 10 to 30 cm and from 20 to 50 cm, respectively. These figures not only meet Galileo HAS requirements but outperform them by achieving instantaneous positioning. Additionally, it is shown that formal errors of the ionospheric corrections, which are also transmitted, are typically at the decimeter level (1 sigma), protecting users against erroneous position by weighting its measurements in the navigation filter.

Abstract

European Galileo officially provided global users with the initial high accuracy service (HAS) through Galileo satellites for free on January 24, 2023. The emergence of the Galileo HAS provides the possibility of a globally stable real-time precise point positioning (PPP) time transfer that does not depend on a network. The coverage and service availability (the proportion of epochs that can support PPP solution) of the HAS, accuracy of the satellite orbit and clock offset, and accuracy of HAS time transfer were comprehensively analyzed using real-time and satellite-broadcast HAS data. Twenty-three global time links, including time-keeping laboratories, were established to assess the accuracy of time comparison using the HAS product. The results showed that the average numbers of GPS and Galileo satellites with valid HAS corrections worldwide were 9.44 and 7.95, respectively, and the average service availability of GPS-only and Galileo-only PPP using the HAS product reached 99.9% and 99.6%, respectively, in most areas. Taking post-processing satellite products from GeoForschungZentrum as a reference, the radial errors of the HAS orbit product are concentrated within 5 cm, and the accuracy of the corrected Galileo satellite clock offset was twice that of ephemeris. Further, in the 7200–12295 km-long baselines, the mean standard deviation values of HAS GPS-only, Galileo-only, and GPS/Galileo time comparison result errors were 0.19–0.29 ns, 0.13–0.24 ns, and 0.11–0.21 ns, respectively. In general, HAS time transfer is available globally.

Abstract

Satellite clock bias (SCB) is a critical factor influencing the accuracy of real-time precise point positioning. Nevertheless, the utilization of real-time service products, as supplied by the International GNSS Service, may be vulnerable to interruptions or network failures. In specific situations, users may encounter difficulties in obtaining accurate real-time corrections. Our research presents an enhanced predictive model for SCB using a long short-term memory (LSTM) neural network fused with a Self-Attention mechanism to address this challenge. This fusion enables the model to effectively balance global attention and localized feature capture, ultimately enhancing prediction accuracy and stability. We compared and analyzed our proposed model with convolutional neural network (CNN) and LSTM models. This analysis encompasses an assessment of the model's strengths and suitability for predicting SCB within the BeiDou navigation system, considering diverse satellites, orbits, and atomic clocks. Our results exhibit a substantial improvement in predictive accuracy through the LSTM-Attention model. There has been an improvement of 49.67 and 62.51% compared to the CNN and LSTM models in the 12-h prediction task. In the case of the 24-h prediction task, the improvements escalated to 68.41 and 71.16%, respectively.

Abstract

The characteristics of residual errors in GNSS positioning are crucial for fault detection and integrity monitoring. Despite the wide use of the zero-mean Gaussian assumption in the navigation community, studies highlight non-Gaussian traits and heavy-tailed patterns in residual errors. The problem will be even more challenging for users in difficult environments where residual errors consist of a combination of multiple modes with high complexity and cannot be fitted with known distributions or empirical models. To address these issues, our work introduces a novel approach leveraging the Wasserstein distance for assessing the performance of error characterization and fault modeling. However, relying solely on the Wasserstein distance value for direct similarity assessment is hindered by its dependency on dimensionality. We propose a second-order Gaussian Wasserstein distance-based precision metric to offer a quantitative evaluation of GNSS error models in terms of both goodness-of-fit and underlying assumptions. We also establish a robust scoring criterion to distinguish between various GNSS error models, ensuring comprehensive evaluation. The proposed method is validated through a known high-dimensional Gaussian model, achieving a score of 99.95 over 100 with a sample size of 10,000. To demonstrate the capability in dealing with complexity, two multivariate complex GNSS models incorporating copula functions to capture intricate inter-dimensional correlations are established and assessed by our approach. Experimental results show that the method can effectively deliver the evaluation of goodness-of-fault models using the establishment of a universal criteria with different dimensions. It provides a quantitative measure on the goodness of fittings and enhances the modeling to reflect the reality, therefore solving the problems raised above. In addition, with this technique, the close-to-reality fault models can be chosen to generate simulated faulty datasets, thus benefiting algorithm testing and improvement. This is also beneficial to more accurate integrity risk assessment to avoid overbounding- or underbounding-resulted false or missed alert.

Abstract

As part of the International GNSS Service (IGS), several analysis centers provide GPS and Galileo satellite phase bias products to support precise point positioning with ambiguity resolution (PPP-AR). Due to the high correlation with satellite orbits and clock offsets, it is difficult to assess directly the precision of satellite phase bias products. Once outliers exist in satellite phase biases, PPP-AR results are no longer reliable and the combination of satellite phase bias products from IGS analysis centers also gets difficult. In this contribution, we propose a method independent of ground measurements to detect outliers in satellite phase biases by computing the total Difference of satellite Orbits, Clock offsets and narrow-lane Biases at the midnight epoch between two consecutive days. Results over 180 days show that about 0.2, 1.1, 2.0 and 0.1% of the total DOCB values for GPS satellites exceed 0.15 narrow-lane cycles for CODE final, CODE rapid, CNES/CLS final and WUHN rapid satellite products, respectively, while the same outlier-ratios for Galileo satellites are 0.1, 0.9, 0.4 and 0.1%, respectively. As an important contribution to the orbit, clock and bias combination task, we check the consistency of satellite phase bias products between two analysis centers before and after removing these detected outliers from individual analysis centers. It is convincing that the number of large differences of satellite phase biases between two analysis centers is notably reduced.

Abstract

PPP-RTK combines the advantages of both precise point positioning (PPP) and real-time kinematic (RTK) techniques. While constructing PPP-RTK models based on undifferenced and uncombined observations offers apparent benefits, these observation equations suffer from a rank deficiency issue. To address this problem, the Singularity-system (S-system) theory can be utilized. This theory imposes constraints on a minimal subset of parameters, known as the S-basis, by setting them to arbitrary values, typically zeros. Despite the existence of multiple options for the S-basis, prevailing research conventionally selects the parameters of one receiver—the pivot—as the S-basis. In this study, we depart from this practice by selecting the mean of receiver-related parameters as the S-basis. This departure prompts an exploration into how the S-basis choices influence PPP-RTK outcomes regarding network product precision and user positioning performance. Our comparative analysis of the mean receiver (MR) and pivot receiver (PR) models unveils distinctions in the combined product precision. These products include satellite clocks, satellite phase biases, and ionospheric delays (excluding tropospheric delays). The distinction emerges because the estimable satellite clocks in the PR model incorporate atmospheric delays specific to the pivot receiver, in contrast to the MR model, which integrates mean atmospheric delays from all receivers. Despite the distinction in the analytical form of combined product and its precision, both model results in similar positioning performance. This is because variations in product precision levels caused by selecting different atmospheric parameters as the S-basis can be nullified by the parameterized atmospheric delays on the user side. With the inclusion of tropospheric delays, the PR and MR models also demonstrate similar performance and yield more accurate user positioning when located near the pivot receiver compared to positions farther from the pivot receiver when employing ambiguity-float network products. This dependence on the pivot receiver stems from both models selecting the pivot receiver ambiguities as the S-basis, while opting for mean ambiguities across all receivers negates the integer nature of ambiguities. Our conclusion underscores that identical positioning outcomes in PR and MR PPP-RTK models rely on both models selecting the same ambiguities as the S-basis. This highlights the potential variability in PPP-RTK performance when different ambiguity parameters are selected as the S-basis, particularly in the absence of network integer ambiguity resolution.

Abstract

GNSS phase observable-specific signal bias (OSB) corrections are essential for widespread application of precise point positioning with ambiguity resolution (PPP-AR) or PPP-RTK. However, subject to the orbital error effects, conventional undifferenced (UD) model-derived BeiDou System (BDS) real-time (RT) OSB products are usually unsatisfactory. In this study, a novel OSB-generating method assisted by the double-differenced (DD) model is proposed. The reliable integer UD ambiguities are obtained by converting DD ambiguities with given ambiguity datums, by which the RT orbit error effects on ambiguity fixing can be reduced during the OSB extraction and PPP-AR process. Validated using data from two regional sparse GNSS reference networks in Shaanxi, China, and Europe, results show that the proposed method-derived OSB products can improve RT PPP-AR performance effectively. In the Shaanxi network, the narrow-lane ambiguity residuals for BDS-3 within ± 0.25 cycles are improved by 23.1% and 33.2% compared to those using the UD model and Centre National d’Etudes Spatiales (CNES)-derived OSB products, respectively, and the corresponding values are 15.2% and 43.1% in the European network. A centimeter- or even millimeter-level positioning accuracy can be achieved for BDS PPP using the poposed OSB products in both networks. In the kinematic PPP-AR test within the Shanxi network, the mean RMS of the BDS-2/3 fixed solutions in the east, north, and up directions is 0.9, 0.7, and 2.3 cm, with a decrease of 57.1%, 53.3%, and 46.5% compared to that using OSB derived by UD model. The median Time-To-First-Fix (TTFF) is also shortened from 23.8 to 7.5 min.

Abstract

The global navigation satellite system (GNSS) is widely employed in location-based services (LBS) as a pivotal technology for high-precision navigation and positioning. However, measurement errors cannot be fully eliminated in practical applications, potentially impacting positioning accuracy and reliability. Based on robust estimation and fractional calculus, we construct a robust fractional-order extended Kalman filter (RFEKF) model with a Huber function model. First, we introduce a fractional-order extended Kalman filter (FEKF) model. Second, the RFEKF is constructed by incorporating an equivalence weight matrix that introduces redundancy and the statistical properties of predicted residuals. The RFEKF model adapts the gain matrix through iterative adjustment, obtaining optimal solutions and enhancing the operational efficiency of the model. Finally, simulation experiment and practical implementation are carried out to verify the proposed RFEKF model in GNSS navigation and positioning. The results demonstrate that the RFEKF significantly improves the accuracy of navigation and positioning in the presence of gross errors, surpassing the performance of the REKF.

Abstract

Precise Point Positioning-Real Time Kinematic (PPP-RTK), a synthesis of PPP and RTK techniques, is a highly precise positioning technique that has been extensively studied in the GNSS community. The Zenith Wet Delay (ZWD), an important estimated parameter in PPP-RTK, significantly impacts the user positioning performance. It is crucial to determine the optimal ZWD processing methods for PPP-RTK. The present study contributes to this research trend by designing and contrasting four ZWD processing strategies for PPP-RTK, confirming the benefits of Global Forecast System (GFS) products (a global numerical weather prediction (NWP) model) in both the network and user models of PPP-RTK. In this study, the full-rank ionosphere-float undifferenced and uncombined (UDUC) PPP-RTK network and user models are first derived. To determine the optimal ZWD processing method in PPP-RTK, four ZWD processing strategies are designed and a comprehensive positioning performance evaluation is conducted for the four different ZWD processing strategies using 10-day GPS observation data from 55 GPS stations in the USA. The results show that a priori GFS ZWD information into the PPP-RTK network model’s ZWD constraint significantly improves the user positioning performance. With the GFS ZWD constraint, for user stations, the average convergence time after network filter restart is reduced by 12.7%, from 55 to 48 min, the average vertical positioning RMS of the float ambiguity solution (including convergence time) is reduced by 18.8%, from 8.5 to 6.9 cm. Additionally, a priori GFS ZWD information can also offer users an absolute ZWD constraint option, which does not require communication with the network, achieving performance comparable to PPP-RTK network ZWD products.

Abstract

The China BeiDou Navigation Satellite System (BDS-3) is recognized for its advantages compared to BDS-2. However, the enhancement of performance through the simultaneous utilization of BDS-2 and BDS-3 in real-time kinematic (RTK) applications remains insufficiently investigated. Herein, we developed an overlapping triple-frequency (TF) BDS-3/BDS-2/inertial navigation system (INS) tightly coupled (TC) integration model that takes advantage of the BDS-3/BDS-2 overlapping frequencies of B1I/B2b(B2I)/B3I for intersystem combination and INS-assisted positioning. An analytical formula for ambiguity dilution of precision (ADOP) was derived, serving as the foundation for an exploration into how multi-frequency measurements and INS assistance affect ambiguity resolution (AR). A vehicle experiment was conducted in a city to evaluate the performance of the measurement models for various frequencies, available satellites, and INS assistance. Analysis of the double differencing errors of the pseudorange and carrier phases revealed that a robust model in kinematic situations is preferred over static situations. AR ability was assessed regarding ADOP, ratio test, and success rates, and the positioning and attitude determination results were examined. Overall, the characteristics of the ADOP analytic formula and processing results produce a similar conclusion regarding the contribution of multi-frequency, available measurements, and INS assistance on AR; further, they provide reference values for moving measuring system users to implement the optimal model in kinematic situations.

Abstract

Low-cost sensor navigation has risen in the past decade with the onset of many modern applications that demand decimeter-level accuracy using mass-market sensors. The key advantage of the precise pointing positioning (PPP) technique over real-time kinematic (RTK) is the non-requirement of local infrastructure and still being able to attain decimeter to sub-meter level accuracy while using mass-market low-cost sensors. Achieving decimeter to sub-meter-level accuracy is a challenge in urban environments. Therefore, adaptive filtering for low-cost sensors is necessary along with motion-based constraining and atmosphere constraints. The traditional robust adaptive Kalman filter (RAKF) uses empirical limits that are derived by analyzing the GNSS receiver data learning statistics based on confidence intervals beforehand to determine when the adaptive factor needs to be applied. In this research, a new technique is proposed to determine the adaptive factor computation based on the detection of an increase in the number of satellite signals after a partial outage. The proposed method provides 6–46% better accuracy than the traditional RAKF and 11–55% better accuracy performance when compared to a tightly coupled solution without enhancements when multiple datasets were tested. The results prove to be a significant improvement for the next generation of applications, such as low-autonomous and intelligent transportation systems.

Abstract

Multipath (MP) reception has been among the main issues for accurate and reliable positioning in urban environments. It has been shown to introduce positioning errors of up to tens of meters for conventional two-step (2SP) receivers. The direct position estimation (DPE) has been introduced as a more robust positioning algorithm compared to the conventional two-step (2SP) receivers in terms of MP mitigation. However, its high computational load prevents DPE from real-time positioning for commercial receivers. Thus, we present a novel grid-based maximum likelihood estimation (MLE) algorithm based on DPE by making use of pseudorange measurements to obtain the correlogram on a predefined searching space. Unlike DPE, which performs correlations at the intermediate frequency (IF) level, correlations are done by directly comparing the code phase of each candidate position, velocity, and timing with the incoming pseudorange. This way, the proposed method retains MP mitigation properties from DPE through the use of MLE from DPE and allows for a significantly reduced computational load compared to DPE. The proposed method was tested with both open-sourced datasets collected in urban environments as well as IF simulation data, and its performance is evaluated against a 2SP receiver. Results show that the proposed method manages to acquire the MP mitigation capability of DPE and outperforms 2SP by up to around 23% in the tested urban datasets and 91% in the simulation data, at a much-reduced computational time. The resilience of our proposed method against MP and NLOS could even potentially offer applications in geodetic networks, where robust estimators are traditionally employed to counteract outliers. 

Abstract

With the increasing number of low-cost GNSS antennas available on the market, there is a lack of comprehensive analysis and intercomparison of their performance. Moreover, multi-GNSS observation noises are not well recognized for low-cost receivers. This study characterizes the quality of GNSS signals acquired by low-cost GNSS receivers equipped with eight types of antennas in terms of signal acquisition, multipath error and receiver noise. The differences between various types of low-cost antennas are non-negligible, with helical antennas underperforming in every respect. Compared with a geodetic-grade station, GPS and Galileo signals acquired by low-cost receivers are typically weaker by 3–9 dB-Hz. While the L1, E1 and E5b signals are well-tracked, only 72% and 86% of L2 signals are acquired for GPS and GLONASS, respectively. The signal noise for pseudoranges varies from 0.12 m for Galileo E5b to over 0.30 m for GLONASS L1 and L2, whereas for carrier-phase observations it oscillates around 1 mm for both GPS and Galileo frequencies, but exceeds 3 mm for both GLONASS frequencies. Antenna phase center offsets (PCOs) vary significantly between frequencies and constellations, and do not agree between two antennas of the same type by up to 25 mm in the vertical component. After a field calibration a of low-cost antenna and consistent application of PCOs, the horizontal and vertical accuracy is improved to a few millimeter and a few centimeter level for the multi-GNSS processing with double-differenced and undifferenced approach, respectively. Last but not least, we demonstrate that PPP-AR is possible also with low-cost GNSS receivers and antennas, and improves the precision and convergence time. The results prove that selection of low-cost antenna for a low-cost GNSS receiver is of great importance in precise positioning applications.