GNSS Signals Research Articles

Pseudorange characteristics of soil-transmitted GNSS signals and their application in soil moisture content retrieval

Pseudorange characteristics of soil-transmitted GNSS signals and their application in soil moisture content retrieval

Analysis of Multifrequency GNSS Signals and an Improved Single-Epoch RTK Method for Medium-Long Baseline

In recent years, the quantity of visible satellites has increased significantly due to multiple satellite systems that leaped forward. The BeiDou Navigation Satellite System (BDS) and Galileo satellite navigation system (Galileo) broadcast triple-frequency signals and above to users, thus enhancing the reliability, continuity, and availability of the single-epoch real-time kinematic (RTK) positioning. In this study, an improved single-epoch multifrequency multisystem RTK method is successfully developed for the medium-long baseline. First, the Galileo and BDS extra-wide-lane (EWL) ambiguities are fixed at a high success rate, and the Galileo and BDS wide-lane (WL) ambiguity is achieved via the transformation process. Second, the fixed WL ambiguities of Galileo and BDS are exploited to elevate the fixed rate of GPS WL ambiguity. Third, the parametric strategies for ionospheric delay are carried out to upregulate the narrow-lane (NL) ambiguity-fixed rate of GPS. Further, the real-time data are adopted for verifying the feasibility of the method developed in this study. The experimental results demonstrate the optimal carrier-to-noise density ratio (C/N0) of full operational capability (FOC) E5a/E5b at all frequencies, followed by IIR-M L1, and IIR-A/B L2 exhibits the worst performance. Generally, the multipath combination (MPC) of Galileo signals shows root mean square (RMS) values within 0.4 m, ordered as follows: E 1 > E 5 b > E 5 a . For the BDS-2, the B3 signal exhibits optimal performance, while the B1 signal is the worst. The RMS of MPC errors of L1 signals is smaller than the L2 signals for the GPS. Furthermore, under the 50 km baseline, the GPS NL ambiguity-fixed rate using the ionosphere-free (IF) combination reaches only 47.74% at the ratio threshold of 2. Finally, compared to the ionosphere-free combination method, the GPS NL ambiguity-fixed rate is increased by 45.52% with the presented method. The proposed approach broadens the future application of deformation monitoring in medium-long baseline scenarios.

Water level measurements using reflected GNSS signals

GNSS Interferometric Reflectometry (GNSS-IR) is a method that can be used to measure water levels. The frequency of the interference pattern created by direct and reflected GNSS signals is used to estimate the height of the GNSS antenna above the reflecting surface. In principle each rising and setting satellite arc that reflects off the water can be used, yielding ~350 water level measurements per day at sites that track the four major constellations and have a good view of the water. Two examples of the GNSS-IR method are presented, one at a coastal site in Australia and the other from the Ems river in Germany. Each site has collocated traditional tide gauge instrumentation. Open source GNSS-IR software is used to analyze the GNSS data from each site. Correlations between GNSS-IR with the traditional gauge are shown to be better than 0.99.

Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space

Coherent observations in GNSS reflectometry are prominent in regions with smooth reflecting surfaces and at grazing elevation angles. However, within these lower elevation ranges, GNSS signals traverse a more extensive atmospheric path, and increased ionospheric effects (e.g., delay biases) are expected. These biases can be mitigated by employing dual-frequency receivers or models tailored for single-frequency receivers. In preparation for the single-frequency GNSS-R ESA “PRETTY” mission, this study aims to characterize ionospheric effects under variable parameter conditions: elevation angles in the grazing range (5° to 30°), latitude-dependent regions (north, tropic, south) and diurnal changes (day and nighttime). The investigation employs simulations using orbit data from Spire Global Inc.’s Lemur-2 CubeSat constellation at the solar minimum (F10.7 index at 75) on March, 2021. Changes towards higher solar activity are accounted for with an additional scenario (F10.7 index at 180) on March, 2023. The electron density associated with each reflection event is determined using the Neustrelitz Electron Density Model (NEDM2020) and the NeQuick 2 model. The results from periods of low solar activity reveal fluctuations of up to approximately 300 TECUs in slant total electron content, 19 m in relative ionospheric delay for the GPS L1 frequency, 2 Hz in Doppler shifts, and variations in the peak electron density height ranging from 215 to 330 km. Sea surface height uncertainty associated with ionospheric model-based corrections in group delay altimetric inversion can reach a standard deviation at the meter level.

TERRESTRIAL 3D MAPPING OF FORESTS: GEOREFERENCING CHALLENGES AND SENSORS COMPARISONS

Abstract. Terrestrial 3D reconstruction is a research topic that has recently received significant attention in the forestry sector. This practice enables the acquisition of high-quality 3D data, which can be used not only to derive physical forest criteria such as tree positions and diameters, but also more detailed analyses related to ecological parameters such as habitat availability and biomass. However, several challenges must be addressed before fully integrating this technology into forestry practices. The primary challenge is accurately georeferencing surveyed 3D data acquired in the same location and placing them into a national projection reference system. Unfortunately, due to the forest canopy, the GNSS signal is often obstructed, and it cannot guarantee sub-meter accuracy. In this paper, we have implemented an indirect georeferencing methodology based on spheres with known coordinates placed at the forest’s edge where GNSS reception was more reliable and accurate than under the canopy. We evaluated its performance through three analyses that confirmed the validity of our approach. Indeed, the accuracy of the TLS point cloud, georeferenced using our method, is within a centimetre level (4.7 cm), whereas mobile scanning methods demonstrate accuracy within the decimetre range but still less than a metre. Additionally, we have initiated the analysis of a potential future application for mixed reality headsets, which could enable real-time acquisition and visualisation of 3D data.

Impact of using type mean vs. individual receiver antenna PCC in multi-GNSS PPP

In the paper, the differences between position components obtained using the precise point positioning (PPP) technique with individual and type mean PCC models were investigated. Daily GNSS observations from ten selected European Permanent Network (EPN) stations were used in the study. Eight different combinations of GPS, Galileo, BeiDou and GLONASS systems observations were proposed for analyses. The observation processing was done using the open-source software package GAMP. The results show that differences in the calibration models propagate to the position domain. Position offsets, resulting from the use of individual calibrations instead of the type mean, reach up to 10 mm in the vertical component while are generally keeping below 2 mm in the horizontal ones. The analysis also shows that adapting GPS PCCs to other GNSS (e.g. Galileo or BeiDou) signals does not significantly increase achieved position component differences.

A machine-learning approach to estimate satellite-based position errors

Abstract Satellite-based navigation systems are widely used in transportation. GNSS signal’s strength or quality can easily be degraded by local environments. As a result, the position accuracy of satellite-based navigation systems decreases. In this paper, a novel approach for estimating the positioning error is proposed using ML/DL technique. For learning the relationship between position errors and increased data from GNSS receivers without any prior experience, neural networks have become the machine learning option of choice in the past few years. Signal degradation is best measured by dilution of precision, elevation angles, and carrier-to-noise ratios. To estimate the position error of satellite-based navigation systems, neural networks are trained in this paper. This paper applies a long-short-term memory (LSTM) network to model the temporal correlation of position error measurements. Therefore, neural networks are capable of learning the trend of position errors through training.

Studying the Fixing Rate of GPS PPP Ambiguity Resolution Under Different Geomagnetic Storm Intensities

AbstractGlobal Positioning System (GPS) Precise Point Positioning (PPP) with correct fixing ambiguity resolution (AR) can reach cm‐mm level positioning accuracy. However, this accuracy can be degraded by the geomagnetic storm effects. To comprehensively investigate the ambiguity resolved percentage (ARP) of GPS kinematic PPP, referred to as PPP‐ARP, under different intensities of geomagnetic storms, based on the Natural Resources Canada's Canadian Spatial Reference System (CSRS) PPP, this study for the first time gives the correlation between the PPP‐ARP and storm intensity using 67 storms occurred in the past 5 years of 2018–2022. Experimental results indicate that the PPP‐ARP decreases gradually as the increase of geomagnetic storm intensity. Under quiet and low geomagnetic conditions (Dstmin > −50 nT), the PPP‐ARP of global GNSS stations can achieve more than 96%, while these during strong storms (Dstmin ≤ −100 nT) are generally lower than 90.0%, especially for the PPP‐ARP of some stations located at low latitudes which are lower than 40.0%. The mechanism of PPP‐ARP decrease under geomagnetic storms is mainly due to the cycle slips and even loss of lock of GNSS signals caused by the storms induced ionospheric disturbances and scintillations. In addition, different from many previous studies, we found that the CSRS‐PPP with AR can achieve good positioning accuracy (3D RMS <0.2 m) even under strong geomagnetic storms.

Performance Analysis of Opportunistic ARAIM for Navigation With GNSS Signals Fused With Terrestrial Signals of Opportunity

Performance Analysis of Opportunistic ARAIM for Navigation With GNSS Signals Fused With Terrestrial Signals of Opportunity

An automated multi-constraint joint registration method for mobile LiDAR point cloud in repeated areas

Urban digitalization, autonomous driving, and 3D map construction all benefit greatly from the ability of mobile LiDAR systems (MLS) to swiftly capture centimeter-level accuracy point cloud data of urban scenes. However, the positional accuracy of MLS point clouds is affected by factors such as GNSS signal loss and IMU inertial drift, which can lead to ghosting and layering of point clouds in repeated areas, reducing its usability. Point cloud registration which was developed to solve the MLS localization problem has been studied for a long time. Most current registration methods only consider the point clouds globally, so the registration of repeated areas has not received adequate attention; and the few methods that target repeated areas fail to balance registration accuracy and automation. To address the above-mentioned issues, we propose an automatic multi-constraint joint registration method for MLS point clouds in repeated areas. Firstly, based on the strict correspondence between trajectory and point clouds, this method identifies point cloud repeated areas by searching for repeated areas in the trajectory. Secondly, it adaptively segments the point clouds of repeated areas based on changes in trajectory angle and speed. Then, it uses a deep learning network to automatically extract the scene features of these segmented point clouds, which are essentially constrained by a variety of basic features such as points, lines, and surfaces. Finally, these multi-constrained scene features are used to perform joint registration of point clouds in repeated areas. We carried out repeated areas registration experiments in a campus scene using our self-developed MLS. The results show that the best registration combination can produce errors of 0.021 m and 0.033 m, respectively, compared with pre-registration errors of 0.094 m and 0.103 m. Our method specifically considers MLS point clouds in repeated areas and ensures satisfactory accuracy while achieving automatic point cloud registration. It is also expected that the proposed method can be extended to other LiDAR platforms that also have trajectories, such as Airborne Laser Scanning (ALS) and Simultaneous Localization and Mapping (SLAM), to realize high-precision automatic registration of point clouds in repeated areas, with little adaptation to the complex scenes.

Evaluation of GNSS reflectometry method for sea level estimation in Indonesia

Over the last decade, GNSS-R (Global Navigation Satellite System reflectometry) has emerged as a technique for observing sea level height using data from GNSS observations. GNSS-R estimates sea level height from the phase center antenna using the reflection of the extracted GNSS signal at sea level. With a large number of tidal stations equipped with GNSS antennas, GNSS-R has the potential to be implemented in Indonesia. GNSS-R observations can also cover sea surface areas tens to hundreds of kilometres away from where the antenna is located over the coast. Furthermore, the installation of a GNSS antenna at a safe height on land allows for the observation of sea levels under extreme conditions such as during storms and cyclones. To employ GNSS-R effectively, several factors must be considered, including signal-to-noise ratio (SNR) data analysis, data processing, filtering control variables, and increased frequency extraction. This study will focus on trying to use GNSS-R method by identifying the best control variables for each study area and evaluating the results. This study used three months of GPS and GLONASS satellite SNR data from two stations, Barus and Morotai. The separated multipath data was then analyzed using the Lomb-Scargle Periodogram (LSP) based on its frequency. The results of GNSS-R sea surface height observations were validated using tide gauge data at both stations. Based on the test results, the RMSE values were 8.7 cm and 8.4 cm at Barus and Morotai stations, respectively. GNSS-R results strongly correlate with tide gauge data, with correlation coefficients of 95% and 98% at Barus and Morotai stations, respectively. Based on these findings, the GNSS-R method can be used to complement tidal sensor data by applying proper quality control.

The CNES Solutions for Improving the Positioning Accuracy with Post-Processed Phase Biases, a Snapshot Mode, and High-Frequency Doppler Measurements Embedded in Recent Advances of the PPP-WIZARD Demonstrator

For many years, the navigation team at the French Space Agency (CNES) has been developing its Precise Point Positioning project. The goal was initially to promote a technique called undifferenced ambiguity resolution. One of the main characteristics of this technique is the capability for a user receiver to perform centimeter-level accuracy in real time. To do so, a demonstrator has been built. Its architecture is composed of three main elements: a correction processing software called the server part, a means to transmit the corrections using standardized messages, and a user software capable of handling the corrections to compute an accurate positioning at the user level. In this paper, we present the recent advances in the CNES precise point positioning demonstrator. They are composed of some evolution of the network of stations and server software, the implementation of the new state space representation standard, a new method for instantaneous ambiguity resolution using uncombined four-frequency signals, its implementation in real-time at the server and the user level, and the use of high-rate Doppler measurements to improve the accuracy of the solution in harsh urban environments. On top of that, the computation of high-accuracy post-processed phase biases with the majority of current GNSS signals supported, compatible with the uncombined method and a new online positioning service to demonstrate the capacity of the user software, is demonstrated.

5G Vs Wi-Fi Indoor Positioning: A Comparative Study

Sensing location information in indoor scenes requires a high accuracy and is a challenging task, mainly because of multipath and NLoS (non-line-of-sight) propagation. GNSS signals cannot penetrate well in indoor environment. Satellite-based navigation and positioning systems cannot therefore be used for indoor positioning.. Other technologies have been suggested for indoor usage, among them, Wi-Fi (802.11) and 5G NR (New Radio). The primary aim of this study is to discuss the advantages and drawbacks of 5G and Wi-Fi positioning techniques for indoor localization.

Lake Altimetry Using Long Coherent Integration Carrier Phase Measurements from Airborne GNSS Reflectometry

Today, land and ocean observations are crucial for monitoring climate change. The method of GNSS reflectometry is an opportunistic way to provide low-cost observations of many geophysical parameters. However, although this method has been the subject of numerous research studies, work is still in progress to improve its possibilities and fields of application. This paper focuses on GNSS reflectometry using carrier phase measurements for water altimetry measurements. The difficulties in implementing such a method lie in the need to collect a coherent signal and to solve the integer ambiguity value. In this context, the implementation of innovative signal processing is described, including the correlation of the reflected signals in dedicated software and the prolongation of the coherent integration time to enhance signal coherency. These processes were applied to data collected over Carcans-Hourtin Lake in France to compute the height of the reflection surface which was then compared to in situ GNSS buoy height measurements. The results show that at 300 ft (91.44 m), the differences between the lake heights measured with the buoy and with the reflectometry data can reach less than 1 cm for the L1, E1 and E5 GNSS signals. In addition, the slope of the geoid estimated with the reflectometry data is very consistent with that of the RAF20 geoid model, with a difference of up to less than 2 mm/km.

An Integrated INS/GNSS System With an Attention-Based Deep Network for Drones in GNSS Denied Environments

We propose a neural network-based approach to assist positioning in an integrated Inertial Navigation System/Navigation Satellite System (INS/GNSS) during GNSS interruptions. One can aid a navigation system during GNSS outages by substituting the GNSS measurements with a well-trained Neural Network (NN). Outputs of this NN can then be used in a Kalman filtering scheme to acquire the best consistent and concise estimates according to the INS measurements. Since this problem has inherent spatial and temporal aspects, the proposed NN should account for both aspects simultaneously. Convolutional Long Short-term Memory (CLSTM) structure is an excellent candidate to satisfy the aforementioned requirement. The designed CLSTM architecture uses the angular rates and specific forces measured by INS to output the pseudo GNSS position increment covering for the lost GNSS signal. An attention mechanism is added to the final layer of the CLSTM to counter the gradient vanishing problem in long time-series prediction (ACLSTM). A field test utilizing a fixed-wing Unmanned Aerial Vehicle (UAV) is arranged to evaluate the proposed architecture's performance. The field test result shows a significant 3-dimensional positioning improvement during GNSS outages.

Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data

Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm3cm−3, the unbiased RMSE (ubRMSE) is 0.042 cm3cm−3, and the mean absolute error (MAE) is 0.040 cm3cm−3 in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm3cm−3, the ubRMSE is 0.051 cm3cm−3, and the MAE is 0.044 cm3cm−3. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm3cm−3. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors.

A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments.

This paper proposes a novel Blockchain-based indoor navigation system that combines a foot-mounted dual-inertial measurement unit (IMU) setup and a zero-velocity update (ZUPT) algorithm for secure and accurate indoor navigation in GNSS-denied environments. The system estimates the user's position and orientation by fusing the data from two IMUs using an extended Kalman filter (EKF). The ZUPT algorithm is employed to detect and correct the error introduced by sensor drift during zero-velocity intervals, thus enhancing the accuracy of the position estimate. The proposed Low SWaP-C blockchain-based decentralized architecture ensures the security and trustworthiness of the system by providing an immutable and distributed ledger to store and verify the sensor data and navigation solutions. The proposed system is suitable for various indoor navigation applications, including autonomous vehicles, robots, and human tracking. The experimental results provide clear and compelling evidence of the effectiveness of the proposed system in ensuring the integrity, privacy, and security of navigation data through the utilization of blockchain technology. The system exhibits an impressive ability to process more than 680 transactions per second within the Hyperledger-Fabric framework. Furthermore, it demonstrates exceptional accuracy and robustness, with a mean RMSE error of 1.2 m and a peak RMSE of 3.2 during a 20 min test. By eliminating the reliance on external signals or infrastructure, the system offers an innovative, practical, and secure solution for indoor navigation in environments where GNSS signals are unavailable.

Positioning performance analysis of real-time BDS-3 PPP-B2b/INS tightly coupled integration in urban environments

Since 2020, the BeiDou-3 global navigation satellite system has provided real-time precision single-point positioning enhancement services (PPP-B2b). The real-time correction provideded by this service can make real-time precision single-point positioning achieve decimeter-level. However, a dense urban environment may cause frequent interruption of GNSS signals, and PPP-B2b alone cannot achieve continuous precision positioning. This study documents a study that attempts to combine PPP-B2b with an inertial navigation system (INS). A real-time tightly coupled integration model of PPP-B2b/INS based on PPP-B2b services was constructed, and a performance analysis of the tightly coupled integration positioning of PPP-B2b/INS in different urban environments was performed. The experiment shows that in an open-sky environment, the decimeter-level positioning accuracy (North: 31.7; East: 21.3; Up: 39.6 cm and North: 34.1; East: 27.6; Up: 41.0 cm) can only be achieved based on PPP-B2b (BDS only and GPS only), PPP-B2b/INS tightly coupled integration positioning accuracy (North: 17.2; East: 10.3; Up: 19.4 cm), and the convergence time is faster; In a dense urban environment, GNSS signals are frequently interrupted, and the positioning accuracy (North: 141.3; East: 95.2; Up: 156.6 cm and North: 147.5; East: 96.4; Up: 159.3 cm) increases significantly only based on PPP-B2b (BDS only and GPS only), and positioning interruption occurs frequently. However, PPP-B2b/INS tightly coupled integration could still achieve continuous decimetre-level positioning (North: 30.0; East: 28.4; Up: 46.3 cm).

UAV Localization in Low-Altitude GNSS-Denied Environments Based on POI and Store Signage Text Matching in UAV Images

Localization is the most important basic information for unmanned aerial vehicles (UAV) during their missions. Currently, most UAVs use GNSS to calculate their own position. However, when faced with complex electromagnetic interference situations or multipath effects within cities, GNSS signals can be interfered with, resulting in reduced positioning accuracy or even complete unavailability. To avoid this situation, this paper proposes an autonomous UAV localization method for low-altitude urban scenarios based on POI and store signage text matching (LPS) in UAV images. The text information of the store signage is first extracted from the UAV images and then matched with the name of the POI data. Finally, the scene location of the UAV images is determined using multiple POIs jointly. Multiple corner points of the store signage in a single image are used as control points to the UAV position. As verified by real flight data, our method can achieve stable UAV autonomous localization with a positioning error of around 13 m without knowing the exact initial position of the UAV at take-off. The positioning effect is better than that of ORB-SLAM2 in long-distance flight, and the positioning error is not affected by text recognition accuracy and does not accumulate with flight time and distance. Combined with an inertial navigation system, it may be able to maintain high-accuracy positioning for UAVs for a long time and can be used as an alternative to GNSS in ultra-low-altitude urban environments.

Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments.

This research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal outages. The system features the use of an integrated INS/monocular visual simultaneous localization and mapping (SLAM) navigation system that takes advantage of LiDAR depth measurements to correct the scale ambiguity that results from monocular visual odometry. The proposed system was tested using two datasets, namely, the KITTI and the Leddar PixSet, which cover a wide range of driving environments. The system yielded an average reduction in the root-mean-square error (RMSE) of about 80% and 92% in the horizontal and upward directions, respectively. The proposed system was compared with an INS/monocular visual SLAM/LiDAR SLAM integration and to some state-of-the-art SLAM algorithms.