Smartphones, being one of the most ubiquitous sensors in daily life, have the capability to receive Global Navigation Satellite System (GNSS) signals, thereby enabling them to provide location-based services (LBSs) for mass-market users. Spatial information is one of the vital components in intelligent transportation and internet of things applications, and transportation-related applications like lane-level navigation are among the most frequently used smartphone LBSs. Considering the high noise level of smartphone GNSS measurements in such applications, there is a risk of selecting a reference satellite with measurement outliers in relative positioning technology, which would therefore decrease positioning accuracy and reliability. To address this issue, this paper proposes a resilient reference satellite configuration in smartphone relative positioning, where two reference satellites are selected per frequency for each constellation, accompanied by an automatic switching strategy between single and dual-reference satellite configurations. The proposed method extends the observation equations with a second reference satellite, and is validated with 18 datasets collected in driving environments, and both theoretical analysis and positioning results demonstrate that the dual-reference satellite configuration outperforms conventional single-reference satellite strategies except in extremely harsh environments. When applying the resilient switch, the percentage of horizontal positioning errors within 4 meters is largely improved. Moreover, the 68th percentile horizontal positioning errors are reduced by ~3 decimeters compared to single reference satellite method, and the percentages of positioning errors within 1.0 and 1.5 meters are improved by 8% and 9%, respectively, indicating a higher capability and great potential of providing lane-level navigation with the proposed resilient reference satellite configuration.

Urban environments, characterized by dense high-rise buildings and narrow streets, present substantial challenges to GNSS positioning due to signal blockages and multipath effects. The conventional fault detection and exclusion (FDE) methods struggle in these settings because the majority of measurements contain multipath or non-line-of-sight (NLOS) errors. To address these challenges, a novel pedestrian dead reckoning (PDR)-aided FDE framework is proposed to enhance GNSS positioning accuracy in urban canyons. In this framework, the constraints are firstly derived from PDR and the receiver clock error, and these constraints are then used to enhance GNSS positioning through clustering. Both static and dynamic tests were carried out to evaluate the performance of the proposed system. Results show that the proposed approach achieves accuracies of 1.7m–11.5m, compared to 4.2m–57.4m for chip outputs, while the conventional FDE method is impractical in deep urban canyons since its availability extremely decreased to 3.2%. Kinematic tests in Hong Kong reveal a 52.4% enhancement in root mean square (RMS) accuracy (12.9m vs. 27.6m for chip outputs) with 100% availability. This work provides a computationally efficient, hardware-independent solution for reliable urban positioning on consumer devices.

Human Activity Recognition (HAR) has become increasingly important in healthcare, smart homes, and human–computer interaction applications. However, traditional vision-based approaches suffer from privacy concerns and high deployment costs, while single-sensor methods are often limited in robustness and generalization capability. To address these challenges, this study proposes a multimodal HAR framework that integrates a 2×2 Wi-Fi Channel State Information (CSI) array with wearable inertial sensors. The proposed 2×2 CSI array enables synchronized multi-channel acquisition and fusion, improving signal stability and reducing packet loss in complex indoor environments. Meanwhile, accelerometer and gyroscope data are collected from a smartwatch and combined with CSI signals to construct a comprehensive multimodal representation. A hierarchical deep learning architecture, termed M2HAR-Net, is designed to effectively extract and fuse spatial–temporal features from heterogeneous modalities, capturing complementary motion characteristics. To enhance computational efficiency and real-time responsiveness, an ablation study on PCA-based frequency-domain reduction demonstrates that retaining only the first principal component preserves discriminative information while significantly reducing inference overhead. Experimental results on a dataset collected from four participants show that the proposed method achieves an overall accuracy of 98.77% across nine daily activities. Comparative evaluations with several state-of-the-art models further validate the effectiveness and robustness of the proposed framework. These findings indicate that efficient multimodal fusion can substantially improve HAR performance, while future work will focus on cross-environment generalization and large-scale validation.

This study reports the unusual decimeter-scale elastic ground deformations observed by the Global Navigation Satellite System (GNSS) station TONG in Nuku’alofa after the tremendous January 15, 2022 Hunga-Tonga eruption. There are significant uplifts with a maximum of 0.35 m in the vertical direction; the elastic deformations are also obvious with a maximum of 0.15 m in the south direction while are very tiny in the east direction. In addition, the occurred period of the elastic deformations is consistent to the eruption-induced tsunamis recorded in a nearby tide gauge station. The characteristics of the observed elastic deformations confirm their link to the Hunga-Tonga eruption. The possible cause for the elastic deformations is the sustained thrusts from the eruption.

Ray-tracing through numerical weather models (NWMs) is one of the most accurate methods for determining slant tropospheric delays (STDs) in microwave remote sensing. However, the massive data volumes of high-resolution NWMs create substantial I/O operations, limiting large-scale ray-tracing on general hardware. This constraint has historically necessitated parameterized tropospheric delay models, which are disseminated as standardized products (e.g., zenith delays with mapping functions and horizontal gradients). Recently, the AI-driven VAE-former algorithm revolutionized NWM compression, achieving >470:1 ratios by compressing 37 pressure level, 0.25°×0.25° ERA5 data into files smaller than surface-only VMF3 products (1°×1° resolution). This breakthrough challenges the conventional reliance on parameterized models as the sole practical solution. We quantified discrepancies in tropospheric delay parameters between original ERA5 and VAEformer-compressed CRA5 data across 2022, evaluating compression fidelity on global grids and against in-situ zenith tropospheric delay (ZTD) estimates. Results show global average precision loss from compression is10 mm). Our findings demonstrate CRA5 as a reliable ERA5 substitute, with compression-induced inaccuracies being negligible for most microwave-based remote sensing applications. This work underscores that parameterized delay modeling is no longer the exclusive pathway, enabling efficient local computation of high-precision STDs without through mapping functions and gradients.

The rising demand for location-based services (LBS) underscores the critical need for accurate and ubiquitous indoor positioning systems, essential for supporting a wide array of applications across various industries. Over the past two decades, fingerprinting-based positioning methods utilizing pervasive WiFi received signal strength measurements have provided enhanced solutions. However, the manual creation and maintenance of fingerprinting databases are highly labor-intensive and significantly limit scalability. Crowdsourcing offers a scalable solution by engaging regular users in the creation of offline databases, thus eliminating the necessity for expert involvement. Despite this, challenges remain in the need for localization adjustments and calibration sources to ensure the generation of reliable offline databases. Most studies depend on external sources, such as floor plans, deployed anchor nodes, or feedback from active users, which can impede the development of a ubiquitous system. In this paper, we propose leveraging crowdsourced data accumulated over time to automatically infer the positions, and propagation characteristics of fixed WiFi access points to act as anchors with known locations to align and calibrate the crowdsourced traces. The inferred pervasive anchor nodes improved the fingerprints localization and expanded the radio map coverage.

The rapidly increasing demand for high-precision positioning has prompted researchers to develop real-time kinematic (RTK) techniques. In urban areas, however, global navigation satellite system (GNSS) signals are susceptible to obstruction, reflection, and diffraction by dense foliage and buildings, leading to a reduction in the number of tracked satellites and a degradation of GNSS raw measurements due to nonline-of-sight (NLOS) signals and multipath errors. This, in turn, increases the difficulty of accurately resolving integer ambiguities. To address this issue, this article proposes a combined strategy to exclude satellites contaminated by NLOS or multipath. Based on the combined strategy, two single-epoch ambiguity resolution methods, proposed method 1 (PM1) and proposed method 2 (PM2), are introduced. Three kinematic field tests conducted in different typical urban environments are used to validate the effectiveness of the proposed strategy. The correctly fixed means a less than 0.1 m 3-D positional error. The results indicate that, with a mask angle of 10°, the correctly fixed rates of PM1 and PM2 are 96.0% and 97.0% in scenario 1, 63.9% and 64.4% in scenario 2, and 55.9% and 62.7% in scenario 3, respectively. These rates are higher than those of comparative method 1 (CM1) and comparative method 2 (CM2), which are 79.6% and 85.7%, 32.9% and 45.0%, and 29.9% and 45.0% in scenarios 1, 2, and 3, respectively. When the mask angle increases to 20°, the correctly fixed rates of PM1 and PM2 are 96.8% and 97.6% in scenario 1, 63.9% and 64.5% in scenario 2, and 56.8% and 63.9% in scenario 3, respectively. Compared to CM1 and CM2, this represents an improvement of between 7.8 and 27.4% points.

Foot-mounted inertial pedestrian positioning system (FIPPS) is increasingly important in the Internet of Things (IoT) and smart cities because of the passive, anti-interference, and fully automatic advantages. However, FIPPS faces the problem of unknown initial positions and accumulative errors in practical applications, which makes the positioning trajectory inaccurate and unable to be matched in a well-defined coordinate system. A seamless indoor–outdoor inertial pedestrian positioning method based on smartphone precise point positioning (PPP)/3-D map/barometer augmentation is proposed to solve this problem. The main contributions include the following: 1) a 3-D-mapping-aided PPP (3DMA PPP) method is proposed to detect and eliminate non-line-of-sight (NLOS) signals to provide high-precision initial coordinates for FIPPS, which enables positioning trajectories to be matched in the unified WGS-84 coordinate system; 2) the adaptive zero-velocity update (ZUPT) approach based on neuro-fuzzy inference is used to precisely identify the stance phases of various gait patterns to suppress position and velocity error accumulation; 3) a barometer and 3-D building model-based height constraint algorithm is applied to further improve the height estimation of the FIPPS; and 4) an Android application named FYTECH is developed for data transmission between the smartphone and FIPPS and supports the online display of pedestrian positioning trajectories. In a seamless outdoor–indoor experiment with multiple motion patterns, including walking, running, elevators and stairs, we demonstrate that the proposed method can achieve RMS errors better than 1.17 and 1.32 m in the horizontal and vertical directions, respectively. The performance indicates that our method can conveniently fuse multisource sensor information, such as foot wearables, smartphones, and 3-D maps to greatly enhance pedestrians’ seamless indoor–outdoor navigation experience.

Sidewalk-level positions are required for a growing number of pedestrian applications. However, in urban canyons, buildings along both sides of the street severely obstruct global navigation satellite system (GNSS) signals, and the lack of redundant fault-free measurements leads to the poor accuracy in the cross-street direction, posing challenges in determining the side of the street solely based on GNSS positions. While 3-D building models have been utilized to improve position accuracy, particularly in the cross-street direction, techniques relying on these models face issues, such as position ambiguity, high computational load, and low accuracy in the along-street direction. In this study, we aim to develop a novel intelligent urban positioning system using smartphone sensors and pedestrian network. An algorithm is proposed to determine the side of the street by analyzing which half of the sky most of the line-of-sight (LOS) signals are observed. The additional virtual measurement derived from the sidewalk is combined with real measurements to solve GNSS position. It can achieve sidewalk-level positioning since the redundancy in the cross-street direction is significantly improved. The proposed system offers several advantages including elimination of the need for LOS/NLOS signal identification for each satellite and elimination of the need for 3-D building models. Extensive data sets were utilized to train the classification model and evaluate the system’s performance. The results demonstrate a correct identification rate of better than 96% using single epoch GNSS observations. More importantly, the proposed positioning system achieves the accuracy of better than 5 m in urban canyons.

Accurate tropospheric delay forecasts are imperative for microwave-based remote sensing techniques, playing a pivotal role in early warning and forecasting of natural disasters such as tsunamis, heavy rains, and hurricanes. Nevertheless, conventional methods for forecasting tropospheric delays entail substantial computational resources and high network transmission speeds, thereby restricting their real-time applicability in remote sensing operations. In this study, we introduce a novel approach to derive forecasted tropospheric delays using artificial intelligence (AI) weather forecast foundation models (FMs), exemplified by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We assess the accuracy of these forecasts on a global scale employing fifth-generation ECMWF atmospheric re-analysis of the global climate (ERA5) (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5), ground-based Global Navigation Satellite System (GNSS), and in situ radiosonde (RS) measurements as reference data. Our results show that the FM-based scheme outperforms traditional methods in both forecast accuracy and length, with the ability to provide high-accuracy tropospheric delay parameters locally for 15-day forecasts at any location within minutes. Furthermore, the FM scheme still maintains accuracy better than empirical models when forecasting up to ten days in advance. This research demonstrates the potential of AI weather forecast FMs in delivering high-precision tropospheric delay medium-range forecasts and improvements for real-time remote sensing applications.

Global navigation satellite system (GNSS) Doppler measurements are immune to cycle slips, providing a robust way to detect ionospheric variations. In this study, we explore the feasibility of utilizing GNSS Doppler measurements to detect medium-scale traveling ionospheric disturbance (MSTID). First, the method for MSTID detection based on Doppler measurements is introduced. Subsequently, the theoretical formula on the relationship between MSTID signals extracted from Doppler measurements and those obtained from carrier-phase observations is deduced. To validate the method, data from HKSL GNSS station located in Hong Kong during 2018 and 2023 are utilized. The results show the theoretical formula is valid, and the occurrences of MSTID events detected by Doppler measurements are totally consistent to that of by phase measurements. Furthermore, the characteristics of the MSTID occurrence rate closely resemble those observed in previous studies. These results confirm the feasibility and reliability of using GNSS Doppler measurements for MSTID detection.

The technology of autonomous vehicle (AV) is critical in nowadays Intelligent Transportation Systems. To achieve the fully automated operation for AVs, one important prerequisite is the accurate and reliable seamless localization covering complex outdoor-indoor scenarios. Although many solutions have been proposed to support AV localization, it is still challenging in achieving reliable drift-free positioning in seamless urban environments. With the current on-board sensors such as GNSS, IMU, LiDAR and cameras, it is difficult to achieve accurate drift-free indoor positioning due to the lack of GNSS indoors. Meanwhile, challenges remain in reliable navigation under obscured conditions. In this paper, we propose a tightly coupled integration algorithm of GNSS RTK, Ultra-Wide Band (UWB) and Visual Inertial Odometry (VIO) to enhance the accuracy and reliability for AVs seamless localization in challenging environments. The UWB technique is innovatively incorporated into the AVs navigation system to extend absolute positioning indoors. The stereo cameras are utilized to improve positioning continuity and enhance GNSS/UWB usability in outdoor-indoor obscured environments. The proposed algorithm is evaluated over real-world datasets in complex seamless environments. The results show that the proposed algorithm achieves 0.411m and 0.077m horizontal positioning accuracy in obscured outdoor and indoor environments, yielding 71.2% and 18.1% improvements compared with the traditional LC integration schemes, respectively.

The Global Navigation Satellite System (GNSS) has gained widespread use in smartphones, providing support for various pedestrian applications. In urban areas, multipath effects introduce large errors in different measurements, severely degrading the GNSS accuracy. To mitigate multipath effects, several Fault Detection and Exclusion (FDE) methods have been developed. However, in urban canyons, their effectiveness is significantly degraded due to the lack of fault-free measurements in the cross-street direction. In urban canyons, the pedestrian network provides an opportunity to improve the urban GNSS accuracy for pedestrians. The purpose of this study is to improve the GNSS FDE performance through the sidewalk constraints. A new scheme has been proposed to distinguish the correct side of the street effectively. The Hough Transform estimator was used to find the most consistent GNSS measurements under sidewalk constraints. To assess the proposed algorithm’s performance, extensive tests were conducted in urban canyons. The analysis of Carrier-to-Noise density ratio (C/N0) shows that 92% of sidewalks can be distinguished from the opposite sidewalk along the same street. The static test shows that the positioning accuracy can be improved from 22 m to 4.9 m, a 77% improvement over the residual based FDE. The dynamic test showed that the proposed method can achieve the sidewalk positioning, which is essential for many pedestrian applications such as last-mile delivery, emergency caller positioning and jaywalking monitoring.

Smartphones have become an integral part of daily human life and enable almost unlimited coverage of human mobility. Thus, collecting pervasive crowdsourced signatures is feasible. Autonomous localization of such signatures promotes the development of a self-deployable and ubiquitous indoor positioning system (IPS). However, previous crowdsourcing-based IPSs have not considered leveraging such data for developing ubiquitous IPSs. They have relied on methods for data selection and sources for localization adjustment that could work against realizing a ubiquitous system. In contrast, this study introduces a framework Everywhere that leverages crowdsourced data to develop a ubiquitous IPS and addresses existing challenges while developing such systems. Particularly, inertial data selection criteria are proposed to autonomously generate traces with better localization. Moreover, pervasive global navigation satellite system (GNSS) data are leveraged to adjust trace localization, while simultaneously introducing a deploying location (inside elevators) of one anchor node. The node surveys all the floors while reducing the localization error, especially for the buildings surrounded by GNSS-denied areas. Additionally, cumulative data densification is leveraged to realize pervasive resources within the building, thereby boosting trace adjustment and extending database spatial coverage. Furthermore, a better selection of neighboring fingerprints is proposed to enhance online fingerprinting. Such a framework can promote a ubiquitous IPS development for buildings regardless of whether they are surrounded by open sky or GNSS-denied areas.

Positioning, navigation, and timing (PNT) is essential for Internet of Things (IoT) communications and location-based services. Although global navigation satellite system (GNSS) can provide accurate PNT in open areas, obtaining reliable PNT is still a considerable technical challenge in complex urban environments. This is because the GNSS signals are more likely to be affected by multipath interference and nonline of sight (NLOS) reception issues arising from the obstructions and reflections in built environments. These introduce range measurement errors that degrade the GNSS positioning accuracy. This article proposes two resilient pseudorange error prediction and correction strategies to improve the GNSS positioning accuracy in urban environments. In particular, considering the carrier-to-noise density ($C/N$textsubscript 0), satellite elevation angle, and local positional information, the random forest-based pseudorange error prediction and correction models are constructed in two variations, including: 1) the point-based correction (PBC) and 2) the grid-based correction (GBC). The final improved positioning solution is then calculated by using the least square method (LSM) of the corrected pseudoranges. Kinematic test results in urban environments show that both variations of the proposed model can improve the positioning accuracy by 42.9% and 40.8% in horizontal, and by 60.1% and 63.3% in 3-D, respectively, compared to the positioning results obtained by the traditional method without pseudorange error corrections. The improvements are 41.1% and 38.9% in horizontal, and 45.7% and 50.0% in 3-D, respectively, compared with traditional elevation angle weighting method.

In the field of maritime surveillance, the global navigation satellite system (GNSS)-based passive radar has proven its potential for moving target detection (MTD), localization, and velocity estimation. The next stage is to investigate the possibility of obtaining the radar image of the moving ship for target recognition. However, the limited signal power budget of GNSS prevents the conventional inverse synthetic aperture radar technique that is based on target rotational motion and short observation time for GNSS-based passive radar imaging moving target. In this article, a two-stage imaging processing method relying on the target translational motion over a long observation time is proposed. The first stage confirms the presence of the target by a long-time MTD processing technique. In the second stage, based on the analysis of the Doppler history of the target signal in the slow-time domain, short-time Fourier transform and modified random sample consensus are combined to robustly estimate target velocity with reduced computation complexity. To obtain the focused bistatic image, azimuth compression is conducted by using the estimated target velocity. Finally, an image fusion operation is implemented to combine the bistatic images achievable from multiple satellites so that a multistatic image with high quality can be created. The effectiveness of the proposed method is confirmed by the real experimental results of three cargo ships illuminated by several satellites.

Global navigation satellite system (GNSS)-based passive radar suffers from short operational range and low range resolution problems, due to the weak signal strength and relatively narrow signal bandwidths of GNSS signals. To increase range resolution, this letter proposes a joint target detection and range resolution improvement method. First, the long-time integration technique is performed to ensure adequate integration gain for the target response out of background noise. Then, the target detector is exploited to obtain the target response and its peak signal-to-noise ratio (PSNR). After that, the incremental Wiener filter is implemented to efficiently enhance range resolution by using the reciprocal of PSNR as the regularization parameter. The field trial results confirm the effectiveness of the proposed method and indicate that the proposed method can not only provide range resolution enhancement performance better than the truncated singular value decomposition (TSVD) method and the Tikhonov method but also has a higher computational efficiency.

Incremental Structure from Motion (ISfM) has been widely used for UAV image orientation. Its efficiency, however, decreases dramatically due to iterative BA (bundle adjustment). Although the divide-and-conquer strategy has been utilized for efficiency improvement, cluster merging becomes difficult or depends on seriously designed common image poses or 3D points. This paper proposes an algorithm to extract the global model for cluster merging and designs a parallel ISfM solution to achieve efficient and accurate image orientation. First, based on vocabulary tree retrieval, match pairs are selected to construct an undirected weighted match graph, whose edge weights are calculated by considering both the number and distribution of feature matches. Second, an algorithm termed weighted connected dominating set (WCDS), is designed to achieve the simplification of the match graph and build the global model, which incorporates the edge weight in the graph vertex selection and enables the successful reconstruction of the global model. Third, the match graph is simultaneously divided into compact and non-overlapped clusters. After the parallel reconstruction, cluster merging is conducted with the aid of the global model. Finally, by using three UAV datasets that are captured by classical oblique and recent optimized views photogrammetry, the validation of the proposed solution is verified through comprehensive analysis and comparison. The experimental results demonstrate that the proposed parallel ISfM can achieve 17.4 times efficiency improvement and comparative orientation accuracy. In absolute BA, the geo-referencing accuracy is approximately 2.0 and 3.0 times the GSD (Ground Sampling Distance) value in the horizontal and vertical directions, respectively. For parallel ISfM, the proposed solution is a more reliable alternative.