Round-Trip Time (RTT) -based Wi-Fi indoor positioning technology is a critical component of the Internet of Things (IoT), enabling various location-based services in smart buildings. However, the positioning accuracy of RTT will be significantly degraded in environments with severe non-line-of-sight (NLOS) reception and a limited number of access points (APs). To solve this challenge, we propose an indoor positioning method based on visibility matching. Compared to the RTT, which is easily affected by NLOS reception, AP visibility is a new positioning feature determined by RTT and signal strength. Additional constraints can be imposed on the positioning system based on the consistency between the observed visibility and the simulated visibility. To improve the accuracy of RTT ranging, a multi-wall ranging model is adopted that considers the influence of signal penetration through walls. The proposed positioning method combines the refined RTT ranging result and visibility constraint. It mitigates the impact of NLOS receptions on positioning accuracy through the multi-wall ranging model and makes full use of the characteristics of NLOS signal through visibility matching. In different experiments, the proposed method improves median positioning accuracy from 2.3 meters to 0.64 meters compared to other methods.

The recent surge in the development of autonomous vehicles has increased the need for reliable dynamic positioning of road agents in urban areas. Doppler frequency measurement of the global navigation satellite system (GNSS) can provide dynamic information and be used to estimate velocity. However, similar to pseudorange, the accuracy of Doppler frequency is degraded in dense urban areas, due to signal reflections from obstacles, resulting in substantial velocity errors. Existing methods tend to directly exclude non-line-of-sight (NLOS) Doppler frequency, which in turn leads to insufficient measurement numbers. 3D mapping aided (3DMA) GNSS ray-tracing method is commonly used to estimate extra delay from NLOS. The angle of arrival (AOA) of the reflected signal is obtained while tracing the propagation path, which can also be used to simulate NLOS Doppler frequency. Thus, this paper investigates the potential of using NLOS Doppler frequency as a feature for estimating velocity. A novel candidate-based 3DMA GNSS velocity estimation framework is proposed using Doppler frequency to examine the consistency between simulation on each candidate and measurement. Experimental results show NLOS Doppler frequency feature can enhance velocity estimation accuracy, reducing the root-mean-square error of velocity estimation by 46% from 1.06 m/s to 0.57 m/s in dense urban areas.

Accurate positioning technology is essential for various industry and business applications. While indoor and outdoor positioning techniques have been extensively studied, challenges remain in achieving reliable positioning during transitions between these environments. This paper proposes a digital twin-aided positioning correction method to enhance outdoor positioning performance in urban areas, where environmental changes frequently occur. The proposed algorithm simulates positioning solutions for virtual receivers within a grid-based digital twin. By analyzing these simulated positioning errors for each virtual receiver, a statistical model is developed to investigate their positioning characteristics and create a correction information database. Information in this database can be retrieved from the digital twin to the real world and helps improve the positioning performance of GNSS receivers. Importantly, the algorithm is designed to have a low computational load on the receiver side and does not require specially designed antennas, making it suitable for small-sized devices.

Wi-Fi is vulnerable to non-line-of-sight (NLOS) propagation and multipath effects in indoor positioning. This paper proposes a novel access point (AP) visibility matching method for Wi-Fi round-trip-time (RTT) ranging, aiming at reducing positioning errors in complex NLOS indoor environments. This method identifies NLOS signals to determine observation visibility based on the RTT and received signal strength indicator (RSSI). This method also projects the floor plan onto the polar coordinate system to derive simulation visibility. By comparing the consistency between simulation and observation visibility, the method pinpoints where users are most likely located. Experimental evaluations demonstrate that the proposed method achieves positioning accuracy comparable to fingerprinting, without the need for extensive pre-work.

Existing positioning methods have limitations in accuracy and reliability for seamless positioning. Global Navigation Satellite System (GNSS) faces multipath and signal blockage issues, especially indoors. Wi-Fi positioning solutions are mostly restricted to indoors due to large infrastructure requirements. Infrastructure-independent positioning systems, such as visual-inertial navigation systems (VINS), are limited to providing relative pose estimation and are affected by environmental luminance. This paper presents an approach for smartphone seamless positioning by adaptively fusing multi-sensor data from GNSS, Wi-Fi Round Trip Time (RTT) and VINS using factor graph optimization (FGO). The adaptive weighted FGO algorithm optimizes the estimation of the state variables by minimizing the loss function of selected factors with scaled covariance. Combining the strengths of GNSS, Wi-Fi RTT, and VINS, the proposed system achieves improved positioning accuracy and robustness, and experimental results demonstrate our method’s effectiveness in various scenarios.

As the digital twins for the urban area, 3-D building models have been applied in many areas and their reliability has become more and more vital. In a fast-developing city, the scale of construction renewal is usually tremendous with a high frequency, which makes the existing 3-D building models easy to be out-of-date and introduces errors. In this article, we proposed a monitoring algorithm for building updates based on satellite signals. We use satellite measurements to estimate the sky visibility from building blockages. By comparing it to the sky visibility from the existing data set, we can obtain the direction of the building being updated. Finally, the building update directions detected from multiple agents are collaborated in a crowd-sourcing manner to estimate the overall update probability of buildings. A simulation and a real experiment are conducted to evaluate the performance of the proposed algorithm. The proposed monitoring algorithm is robust to sky visibility estimation accuracy, number of agents, and agent positioning error from the simulation analysis. In the real experiment, the building with updates can be accurately detected by the satellite measurements collected from a short pedestrian trajectory around this area.

Urban modeling is one of the most important parts of smart city development and requires the acquisition of urban geometric data, especially those regarding building boundaries. Traditional urban modeling methods rely on LiDAR, oblique photogrammetry, or mobile mapping systems to measure and rectify building geometrical parameters. However, these methods usually require costly devices and a lot of labor. This letter proposes a novel building model rectification method based on a consumer-grade GNSS receiver, which measures the perpendicular distance between building facades and a referencing location by GNSS reflectometry (GNSS-R) and raytracing. The performance of GNSS-R ranging is verified by three experiments with a total station and a LiDAR simultaneously. The results show that the proposed method enabled a smartphone with signal-to-noise ratio (SNR) observations to estimate building facade distances with a mean error of 5 cm. The preliminary results demonstrate the feasibility of this method to achieve precise urban modeling.

Positioning in urban areas is still a challenge due to non-line-of-sight (NLOS) and multipath reception. This paper explores the geometrical characteristics of the GNSS ranging measurement by a graphical representation to better indicate the pseudorange consistency, which can be used to mitigate the NLOS and multipath receptions. The graphical representation is created by the grid-based method combined with the single differenced technique, which is called the single differenced residual map (SDRes Map). With the graphical properties of the SDRes Map, four main focuses of the NLOS/multipath problems, including positioning, signal status prediction, satellite weighting calculation, and NLOS/multipath error calculation, are able to be tackled simultaneously and demonstrated to have superior performance against the conventional or even state-of-the-art method methods.

This paper proposes a novel empirical non-line-of-sight (NLOS) ranging model, called a multi-wall model, for Wi-Fi round-trip-time (RTT) indoor positioning. It is purposely designed to account for NLOS ranging biases due to through-wall signal propagation in complex indoor environments. Also, a low-order polynomial term is added to capture unmodeled NLOS effects. The model takes a 2D geometry floor plan as prior input and learns its parameters from a few on-site training data. It is evaluated by experiments using a large public RTT dataset and our self-collected data and compared to the naive LOS ranging, the polynomial-based empirical model, and a data-driven model. Results show that our model has superior NLOS-ranging performance. At last, we show the application of our model in fingerprinting-based RTT positioning with experiments.

Absolute positioning is an essential factor for the arrival of autonomous driving. At present, GNSS is the indispensable source that can supply initial positioning in the commonly used high definition map-based LiDAR point cloud positioning solution for autonomous driving. However, the non-light-of-sight (NLOS) reception dominates GNSS positioning performance in super-urbanized areas. The recent proposed 3D map aided (3DMA) GNSS can mitigate the majority of the NLOS caused by buildings. However, the same phenomenon caused by moving objects in urban areas is currently not modeled in the 3D geographic information system (GIS). Therefore, we present a novel method to exclude the NLOS receptions caused by a double-decker bus, one of the symbolic tall moving objects in road transportations. To estimate the dimension and orientation of the double-decker buses relative to the GNSS receiver, LiDAR-based perception is utilized. By projecting the relative positions into GNSS Skyplot, the direct transmission path of satellite signals blocked by the moving objects can be identified and excluded from positioning. Finally, GNSS positioning is estimated by the weighted least square (WLS) method based on the remaining satellites after the NLOS exclusion. Both static and dynamic experiments are conducted in Hong Kong. The results show that the proposed NLOS exclusion using LiDAR-based perception can greatly improve the GNSS single point positioning (SPP) performance.

The recent development in vehicle-to-everything (V2X) communication opens a new opportunity to improve the positioning performance of the road users. We explore the benefit of connecting the raw data of the global navigation satellite system (GNSS) from the agents. In urban areas, GNSS positioning is highly degraded due to signal blockage and reflection. 3D building model can play a major role in mitigating the GNSS multipath and non-line-of-sight (NLOS) effects. To combine the benefits of 3D models and V2X, we propose a novel 3D mapping aided (3DMA) GNSS-based collaborative positioning method that makes use of the available surrounding GNSS receivers’ measurements. By complementarily integrating the ray-tracing based 3DMA GNSS and the double difference technique, the random errors (such as multipath and NLOS) are mitigated while eliminating the systematic errors (such as atmospheric delay and satellite clock/orbit biases) between road user. To improve the accuracy and robustness of the collaborative algorithm, factor graph optimization (FGO) is employed to optimize the positioning solutions among agents. Multiple low-cost GNSS receivers are used to collect both static and dynamic data in Hong Kong and to evaluate the proposed algorithm by post-processing. We reduce the GNSS positioning error from over 30 meters to less than 10 meters for road users in a deep urban canyon.

Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS), research in point cloud registration, visual feature matching, and inertia navigation has greatly enhanced the accuracy and robustness of mapping and localization in different scenarios. However, highly urbanized scenes are still challenging: LIDAR- and camera-based methods perform poorly with numerous dynamic objects; the GNSS-based solutions experience signal loss and multi-path problems; the inertia measurement units (IMU) suffer from drifting. Unfortunately, current public datasets either do not adequately address this urban challenge or do not provide enough sensor information related to map-ping and localization. Here we present UrbanLoco: a mapping/localization dataset collected in highly-urbanized environments with a full sensor-suite. The dataset includes 13 trajectories collected in San Francisco and Hong Kong, covering a total length of over 40 kilometers. Our dataset includes a wide variety of urban terrains: urban canyons, bridges, tunnels, sharp turns, etc. More importantly, our dataset includes information from LIDAR, cameras, IMU, and GNSS receivers. Now the dataset is publicly available through the link in the footnote1.

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) spherical colormaps for tract tracing tasks (uniform gray, absolute, eigenmaps, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow colormap led to the same ensemble mean accuracy as other colormaps. However, more than 50 percent of the answers consistently had higher estimates of the ensemble average, independent of the mean values. The number of hues, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that both Boy's surface embedding (greatest spatial resolution and contrast) and absolute colormaps (lowest spatial resolution and contrast) led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. Absolute colormap broadly used in brain science is a good default spherical colormap. We could conclude from our study that human visual processing of a chunk of colors differs from that of single colors.

BACKGROUND: Global scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using interactive visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results. RESULTS: We present ENIGMA-Viewer, an interactive web-based visualization tool for brain scientists to compare statistics such as effect sizes from meta-analysis results on standardized ROIs (regions-of-interest) across multiple studies. The tool incorporates visualization design principles such as focus+context and visual data fusion to enable users to better understand the statistics on brain structures. To demonstrate the usability of the tool, three examples using recent research data are discussed via case studies. CONCLUSIONS: ENIGMA-Viewer supports presentations and communications of brain research results through effective visualization designs. By linking visualizations of both statistics and structures, users can gain more insights into the presented data that are otherwise difficult to obtain. ENIGMA-Viewer is an open-source tool, the source code and sample data are publicly accessible through the NITRC website ( http://www.nitrc.org/projects/enigmaviewer_20 ). The tool can also be directly accessed online ( http://enigma-viewer.org ).