Global Navigation Satellite System Measurements Research Articles

Cooperative Localization under Ionospheric Scintillation Events

Ionospheric scintillation causes major impairments to Global Navigation Satellite System (GNSS) in low-latitude regions. In severe scenarios, this event can lead to complete loss of lock, thus making GNSS measurements unusable for navigation. In this paper, we derive a cooperative localization algorithm where a set of partially connected aircraft exchange messages with neighboring nodes on the network to improve their own position estimates. We consider the scintillation events as abrupt changes in the measurement variance, which are modeled by a discrete-valued Markov process at the nodes which have access to GNSS measurements. Simulation results show that Markovian modeling and cooperation via factor graph message passing reduce the average 3D root mean square localization error and yield an average vertical position error that meets civil aviation standards for approach and landing.

Alignment measurement of immersed tunnels based on laser differential imaging

The precision of underwater alignment for tunnel elements directly impacts the closure state of immersed tunnels. The current global navigation satellite system measurement tower method provides centimeter-level precision; however, this is insufficient due to the tower deformation at great water depths, which fails to meet alignment requirements. In this study, a novel immersed tunnel alignment measurement method using laser differential imaging is developed. A unique sensor layout is designed to measure the axial angle and distance between tunnel elements. The system design is implemented, and its laser data processing scheme is optimized to improve performance base on this sensor layout. The experimental results demonstrate that the accuracy of underwater laser spot extraction is better than 1.16 pixels, and the precision of axial angle and axial distance alignment measurements is better than 25″ and 1.6 mm, with repeatability precision exceeding 10.55″ and 0.53 mm. The laser-based alignment measurement method is not affected by the depth of water, satisfying the required precision of immersed tunnel alignment. These results can be applied to future immersed tunnel installations without measuring towers, providing a novel method for achieving automated and unmanned intelligent construction of immersed tunnels.

A Tightly Coupled GNSS RTK/IMU Integration with GA-BP Neural Network for Challenging Urban Navigation

Abstract Intelligent Transportation System (ITS) is increasing the importance of real-time acquisition of Positioning, Navigation, and Timing (PNT) information from high-accuracy Global Navigation Satellite Systems (GNSS) based on carrier phase observations. The complexity of urban environments, however, means that GNSS signals are prone to reflection, diffraction and blockage by tall buildings, causing a degraded positioning accuracy. To address this issue, we have proposed a tightly coupled single-frequency multi-system single-epoch Real-time Kinematic (RTK) GNSS/Inertial Measurement Unit (IMU) integration algorithm with the assistance of Genetic Algorithm Back Propagation (GA-BP) based on low-cost IMU equipment for challenging urban navigation. Unlike the existing methods, which only use IMU corrections predicted by machine learning as a direct replacement of filtering corrections during GNSS outages, this algorithm introduces a more accurate and efficient IMU corrections prediction model, and it is underpinned by a dual-check GNSS assessment where the weights of GNSS measurements and neural network predictions are adaptively adjusted based on duration of the integrated system GNSS failure, assisting RTK/IMU integration in GNSS outages or malfunction conditions. Field tests demonstrate that the proposed prediction model results in a 68.69% and 69.03% improvement in the Root Mean Square Error (RMSE) in the 2D and 3D component when the training and testing data are collected under 150s GNSS signal-blocked conditions. This corresponds to 52.43% and 51.27% for the signal discontinuously blocked with 500s.

Drought‐Induced Vertical Displacements and Water Loss in the Po River Basin (Northern Italy) From GNSS Measurements

AbstractWe study vertical ground displacement time series from Global Navigation Satellite System (GNSS) stations to measure deformation associated with hydrological drought in the Po river basin. Focusing on interannual trend changes, rather than seasonal (annual) components, we found a clear spatially correlated deformation signal that is temporally (anti)correlated with changes in the Po river level and the SPEI‐12 drought index, with stations moving upward during periods of river/index level decrease and vice versa. In the 2021–2022 time span, which culminated in the most severe drought of the last two centuries, we estimate the amount and spatial distribution of water loss in the basin and its surroundings. Excluding the seasonal signals, between January 2021 and August 2022, the GNSS stations underwent uplift, up to 7 mm, which corresponds to ∼70 Gtons of water loss. Compared to Global Land Data Assimilation System and Gravity Recovery and Climate Experiment estimates, GNSS results show a similar temporal evolution of water content but a more heterogeneous distribution of values. We show that continuous GNSS networks provide an effective way to monitor multiannual trend changes in water storage even in small water basins and serve as a reliable indicator of drought severity.

Enhancing InSAR accuracy: Unveiling more accurate displacement fields through 3-D troposphere tomography

Interferometric Synthetic Aperture Radar (InSAR) measurements are susceptible to tropospheric delay induced by changes in tropospheric temperature, pressure, and water vapor. While pressure and temperature temporal variations have a limited impact on phase gradients, variations in water vapor, which contribute to the wet component of tropospheric delay, play a significant role. Therefore, obtaining a more accurate estimation of the wet refractivity of the tropospheric layer is crucial for improving InSAR displacement field accuracy. This paper presents the utilization of Global Navigation Satellite Systems (GNSS) tropospheric tomography for reconstructing three-dimensional (3-D) wet refractivity. Various data sources are employed, including GNSS measurements, ERA5 reanalysis data, radiosonde observations, and Sentinel-1A radar acquisitions. ERA5 reanalysis data and radiosonde observations are used to calculate tropospheric hydrostatic delay and to evaluate the wet refractivity obtained from tomography, respectively. The effectiveness of tomography techniques in mitigating the tropospheric signal in unwrapped interferograms is compared with the total tropospheric delay obtained from Generic Atmospheric Correction Online Service (GACOS) products, which is one of the most common approach for tropospheric correction of InSAR measurements. To ensure a comprehensive analysis, the effects of the correction methods are investigated in two different weather conditions. Based on the results, GNSS tropospheric tomography reduces the mean Root Mean Square Error (RMSE) by about 1.2 cm compared to GACOS products. This technique proves to be highly effective in improving the accuracy of InSAR displacement field estimation, particularly under challenging weather conditions.

T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.

Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter

High-accuracy heading angle is significant for estimating autonomous vehicle attitude. By integrating GNSS (Global Navigation Satellite System) dual antennas, INS (Inertial Navigation System), and a barometer, a GNSS/INS/Barometer fusion method is proposed to improve vehicle heading angle accuracy. An adaptive Kalman filter (AKF) is designed to fuse the INS error and the GNSS measurement. A random sample consensus (RANSAC) method is proposed to improve the initial heading angle accuracy applied to the INS update. The GNSS heading angle obtained by a dual-antenna orientation algorithm is additionally augmented to the measurement variable. Furthermore, the kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to enhance the accuracy of the measurement model. The heading errors in the open and occluded environment are 0.5418° (RMS) and 0.636° (RMS), which represent reductions of 37.62% and 47.37% compared to the extended Kalman filter (EKF) method, respectively. The experimental results demonstrate that the proposed method effectively improves the vehicle heading angle accuracy.

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Low Earth Orbit (LEO) constellations have recently gained tremendous attention in the navigational field due to their larger constellation size, faster geometry variations, and higher signal power levels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric layers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their lower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects repeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and reflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing real-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS receivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant reduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

A Multi-Depth Deflectometer/Global Navigation Satellite System Method for Measuring Concrete Slab Track Deformation

This study introduces a Multi-Depth Deflectometer (MDD) combined with a Global Navigation Satellite System (GNSS) which was developed for measuring the deformation of railway slab track layers. We newly designed the MDD with the addition of laser sensor modules for increasing precision in which the MDD head is fixed at the top of the track slab. The MDD/GNSS system can measure the relative deflection between each track layer as well as the total deflection at the top of the track slab, which makes it possible to evade the fixed condition problem of classical MDD. The new MDD/GNSS system was installed at the transition zone between a tunnel entrance and its embankment, which experienced high settlement levels prior to repair. The system was used to monitor whether the repaired concrete track foundation with pressurized cement grouting was stabilized effectively and what track layer position was most unstable so that it had the most influence. The GNSS system was designed and built for computing net settlement at each track layer even when the MDD could not be fixed firmly at the end point, which is a major drawback of classical MDD. The results obtained from MDD and GNSS measurements indicated significant potential in aiding railway track settlement measurements.

ASSESSMENT OF STEC ESTIMATION QUALITY USING GNSS PPP FIXED

Abstract. GNSS (Global Navigation Satellite System) data can be used for geodetic remote sensing, particularly for monitoring the ionosphere in the context of Space Weather. One of the important parameters derived from GNSS measurements for ionospheric analysis is the Slant Total Electron Content (STEC). By utilizing GNSS data from multiple frequencies or even a single frequency, the STEC can be computed using an appropriated linear combination, like geometry free. However, when computing an ionospheric gradient between two IPP (Ionospheric Pierce Point) from the same satellite, the precision of the STEC estimate can become a limiting factor. In some cases, the uncertainty in the estimate may be greater than the actual gradient value itself. This poses challenges, especially for augmentation systems like GBAS (Ground Based Augmentation System), where accurate ionospheric gradients are crucial. An alternative approach to improve these limitations is to estimate the STEC using a different approach, like Precise Point Positioning (PPP). For such case, the coordinates of the GNSS stations are constrained to known values (PPP-Fixed), while other parameters such as clock biases, tropospheric delays, and ionospheric delays (including STEC) can be estimated. The results of an experiment carried out to assess the quality of STEC for such application are presented and have shown good results. Ionospheric gradients are agreed in the mm level.

Tracking the water storage and runoff variations in the Paraná basin via GNSS measurements

The Paraná basin is the second largest river basin in South America and provides abundant water resources globally. However, current research lacks hydrological investigation of the region. The vertical crustal deformation recorded by the Global Navigation Satellite System (GNSS) can be used to accurately estimate regional-scale terrestrial water storage (TWS). Therefore, we utilized the daily vertical displacement time series data at 102 GNSS stations to recover the water storage variations in the Paraná basin from 2013 to 2020. To recognize primary spatiotemporal features of TWS changes, we applied the principal component analysis (PCA) method in the inversion strategy. Results indicate that the TWS variations inferred from GNSS generally align in spatiotemporal patterns with estimates from both the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). However, some discrepancies are evident at local scales. The TWS changes derived from both GNSS and GRACE exhibited generally larger magnitude of oscillations than those estimated by GLDAS, while the GRACE results neglected the evident seasonal oscillation of the water mass in the southeast of the basin. Given the challenge of capturing large-scale runoff variations through in-situ observations, we innovatively applied GNSS and water budget closure method to provide a novel runoff estimate for the Paraná basin. The GNSS-inferred runoff exhibited a strong correlation (correlation coefficient of 0.72) with in-situ observations. Overall, our study fills the critical knowledge gap in geodesy-based hydrological investigation in the Paraná basin. We aim to highlight the immense potential of GNSS for hydrological parameter estimation and provide valuable reference data for regional hydrological research and for water resources management.

Automated Detection and Characterization of Wave Structures Obtained From GNSS Measurements

AbstractA simple technique designed to automatically identify and characterize wave structures from total electron content (TEC) data obtained from Global Navigation Satellite System (GNSS) satellites and multiple receiver stations is presented. We used 11 years of GNSS data (one complete solar cycle) to detect and characterize the traveling ionospheric disturbances (TIDs) (or wave structures) over high latitude (65°N ± 5°, 147°W ± 5°—Poker Flat, AK) and middle latitude (40°N ± 5°, 117°W ± 5°—Mt. Moses, NV) regions. The algorithm is capable of automatically detecting basic wave parameters such as wave period, horizontal phase velocity, amplitude, wave propagation direction, and wavelength. The designed algorithm can be applicable in the following areas: (a) global GNSS‐TEC (b) climatology of waves (c) input into innovative machine learning algorithms, such as ABCGAN (e.g., Valentic, 2023, https://doi.org/10.5281/zenodo.7747377 designed to characterize background ionospheric plasma and predict wave‐like high/low‐frequency perturbations) (d) anomaly detection for real‐time scenarios. Furthermore, we apply the developed wave detect and characterization technique to investigate three rockets launched on 26 and 28 January 2015. We examine the distribution of different scales of TIDs, and how they varied from high (Poker Flat) to middle (Mt. Moses) latitudes. Lastly, we show that the wave structures at the high‐latitude regions of Poker Flat are substantially affected by auroral processes and those from the middle‐latitude regions of Mt. Moses are impacted by AGWs coupling from below.

Ionospheric Disturbance in Indonesia during Solar Flare using GPS Observations

Electromagnetic waves transmitted by Global Navigation Satellite System (GNSS) are delayed when they travel through ionosphere layer due to free electrons in that layer. This delay could be used to deduce the variation in ionosphere, through the quantity known as Total Electron Content (TEC). The variability of the TEC in the ionosphere is affected by solar activity, such as solar flares. When a solar flare occurs, which is characterized by a strong and sudden increase in X-ray intensity, it will result in an increase in the electron density in the ionosphere, which is known as ionospheric disturbances. The increase of electron in the ionosphere increases the absorption of HF radio wave energy which can also turn off HF radio communications and cause significant errors in GNSS measurements. This study observed the SID above Indonesia which was disrupted by a solar flare that occurred on November 8, 2013, with class X1.1 angstroms. We use the Sumatran GPS Array (SuGAR) station with observation epochs every 30 seconds to estimate the TEC. The results showed that there was a sudden and simultaneous increase of all observed satellites namely GPS PRN 2, 4, 5, 9, 10, 17, 24, 26 and 28. SID occurred at 11:25:30 LT with a TEC amplitude of 1.05 – 3.94 TEC Unit. The detection time of the SID corresponds to the solar flare’s time observed from the GOES satellite with a delay of ~5 minutes.

GNSS/IMU/LiDAR fusion for vehicle localization in urban driving environments within a consensus framework

The continuous, reliable, and accurate acquisition of position information is a fundamental requirement for autonomous vehicles. It underpins perception, decision-making, and planning modules in automated driving systems. However, the performance of multi-modal sensors, including the Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and LiDAR, typically found in autonomous vehicles, is easily influenced by complex environmental factors encountered in urban driving conditions. This creates challenges for existing localization algorithms. More specifically, while GNSS performs accurately in open-sky areas, it is susceptible to the multi-path effect. Although the IMU-based Inertial Navigation System (INS) provides continuous position data, it suffers from cumulative errors. LiDAR-based map-matching is highly effective when sufficient point cloud features are present but fall short in plain environments. To address these individual sensor deficiencies, we propose the application of a consensus framework for GNSS/IMU/LiDAR fusion, and it is able to be expanded for multi-modal sensor fusion. Our approach seeks to take advantage of the redundancy inherent in these sensor systems. Our method begins with the derivation of the INS mechanization and its error dynamics. Subsequently, based on the Consensus Kalman Filter (CKF), we establish a GNSS measurement node and a Normal Distribution Transformation (NDT) map-matching measurement node. These are designed to estimate position errors in the INS. To counter sensor measurement failure or degradation, we introduce an adaptation mechanism for the CKF. This ensures position accuracy and continuity by normalizing measurement quality across different measurement nodes. Finally, to validate our consensus-based localization framework, we conducted extensive real-world vehicle tests in urban driving conditions. The results have confirmed the superior accuracy and robustness of our approach, even in the face of individual sensor failure. In our experiments, the proposed method reduces the localization mean absolute error by around 17% compared to INS/map-matching fusion, and around 59% compared to INS/GNSS fusion while map-matching fails. While the GNSS and map-matching measurements contain large noises, our framework achieves at least approximately a 30% improvement compared to traditional methods.

Current deformation along the northern Caribbean plate boundary from GNSS measurements in Cuba

Global Navigation Satellite System (GNSS) measurements at episodic sites or continuously operating stations have been used in most actively deforming regions to determine strain rates, fault slip rates, and contribute to assessing regional seismic hazard. However, the central part of the northern Caribbean plate boundary in Cuba has so far been exempt from sufficient GNSS data to study these classic issues of plate boundary kinematics and the associated hazard. Here, we describe the first position-velocity GNSS solution for Cuba and its close surroundings, providing new information on the seismotectonics of the southern boundary of the North American plate. We find that (1) western and central Cuba are rigidly attached to the North American plate, with internal deformation – if any – that is <1 mm/yr, (2) eastern Cuba shows systematic east-trending residual velocities with respect to the North American plate at 2–4 mm/yr, (3) sites on the Cayman Ridge, although located <50 km from the Oriente fault, show negligible residual velocities with respect to the North American plate. A series of kinematic models shows that the data are best explained by a 10 mm/yr slip deficit between the Gonâve microplate and the North American plate accommodated on a vertical, left-lateral Oriente strike-slip fault with a 25 km locking depth along the eastern Cuban margin, and a much shallower locking depth further west along the Cayman Ridge segment of the Oriente fault, which is possibly creeping. The transition from deep to shallow locking coincides with a drastic change in seismicity distribution, possibly indicative of lateral variation in the stress state or fault rheology along the Oriente fault. Additional GNSS observations and longer time series will help consolidate these initial findings and determine whether and how other active faults may contribute to the seismotectonics of Cuba.

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

The seismic hazard from the Lembang Fault, Indonesia, derived from InSAR and GNSS data

Abstract. A growing number of large cities are located near poorly understood faults that have not generated a significant earthquake in recent history. The Lembang Fault is one such fault located near the city of Bandung in West Java, Indonesia. The slip rate on this fault is debated, with estimates ranging from 6 to 1.95–3.45 mm yr−1, derived from a GNSS (global navigation satellite system) campaign and geological measurements respectively. In this paper we measure the surface deformation across the Bandung region and resolve the slip rate across the Lembang Fault using radar interferometry (InSAR – interferometric synthetic aperture radar) analysis of 6 years of Sentinel-1 satellite data and continuous GNSS measurements across the fault. Our slip rate estimate for the fault is 4.7 mm yr−1, with the shallow portions of the fault creeping at 2.2 mm yr−1. Previous studies estimated the return period of large earthquakes on the fault to be between 170–670 years. Assuming simplified fault geometries and a reasonable estimate of the seismogenic depth we derive an estimated moment deficit equivalent to magnitude 6.6–7.0 earthquakes, indicating that the fault poses a very real hazard to the local population. Using the Global Earthquake Model OpenQuake engine we calculate ground motions for these two earthquake scenarios and estimate that 1.9–2.7 million people within the Bandung metropolitan region would be exposed to ground shaking greater than 0.3 g. This study highlights the importance of identifying active faults, understanding their past activity, and measuring the current strain rates of smaller crustal active faults located near large cities in seismic zones.

High‐Density Integrated GNSS and Hydrologic Monitoring Network for Short‐Scale Hydrogeodesy in High Mountain Watersheds

AbstractWe installed a purpose‐built network of co‐located Global Navigation Satellite System (GNSS) stations and meteorological instrumentation to investigate water storage in a high‐mountain watershed along the Idaho‐Montana border. Twelve GNSS stations are distributed across the Selway‐Lochsa watersheds at approximately 30–40 km spacing, filling a critical observational gap between localized point measurements and regional geodetic and satellite data sets. The unique coupling of geodetic and hydrologic observations in this network enables direct comparison between co‐located GNSS measurements of the elastic response of the solid Earth and local changes in measured water storage. This network is specifically designed to address questions of hydrologic storage and movement at the mountain watershed scale. Here, we describe technical details of the network and its deployment; introduce new hydrologic, meteorologic, and geodetic data sets recorded by the network; process and analyze the source data (e.g., time series of daily three‐dimensional GNSS site positions, removal of non‐hydrologic signals); and characterize basic empirical relationships between water storage, water movement, and GNSS‐inferred surface displacement. The network shows preliminary evidence for spatial differences in displacement resulting from a range of snow loads across elevations, but longer and more complete data records are needed to support these initial findings. We also provide examples of additional scientific applications of this network, including estimations of snow depth and snow water equivalent from GNSS multipath reflectometry. Finally, we consider the challenges, limitations, and opportunities of deploying GNSS and weather stations at high elevations with heavy snowpack and offer ideas for technical improvements.

An enhanced outlier processing approach based on the resilient mathematical model compensation in GNSS precise positioning and navigation

The abnormal measurements are widely existent in Global Navigation Satellite System (GNSS) precise positioning and navigation mainly because of the diffraction, reflection, refraction, and even non-line-of-sight reception. However, when multiple outliers exist in GNSS measurements, traditional methods including test procedure or robust estimation usually cannot work well. This study proposed an enhanced outlier processing approach based on the resilient mathematical model compensation. Specifically, first, to avoid excessive deletion, the total number of measurements is considered in the adaptive test procedure with the help of a scale factor. Second, in adaptive robust estimation, the total number of remaining measurements is also considered, thus making it more compatible with the adaptive test procedure. In addition, to overcome the potential inappropriate reweighting operator, different shrinking factors are adopted for code and phase measurements according to their precision, respectively. To verify the effectiveness of the proposed method, one static monitoring experiment and one kinematic vehicle experiment were conducted, where the method without outlier processing, traditional test procedure, traditional robust estimation, and the proposed method were all used. For the static experiment, the ambiguity resolution and positioning solutions of the proposed method perform best. The positioning accuracy of the float and fixed solutions can be improved by approximately 67.4% and 77.6% on average under challenging environments, respectively. For the kinematic experiment, the performance is also the best in terms of positioning availability and accuracy by using the proposed method.

Smartphone MEMS Accelerometer and Gyroscope Measurement Errors: Laboratory Testing and Analysis of the Effects on Positioning Performance.

Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INSs have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone.