Global Navigation Satellite Systems (GNSS) have increasingly been integrated into Internet of Things (IoT) applications in recent years. Since IoT devices are typically compact and equipped with small batteries, achieving a balance between power efficiency and positioning accuracy is crucial. Snapshot positioning represents a state-of-the-art technique for enabling ultra-low power GNSS positioning. However, prior studies have primarily focused on developing snapshot positioning algorithms tailored to specific applications, and these algorithms are typically not benchmarked for positioning accuracy. Recent IoT applications, such as asset tracking in urban canyons and remote healthcare monitoring for chronically ill patients, have shifted this paradigm, making positioning accuracy crucial even for the snapshot approach. In this work, a fully coherent L5-band-based snapshot positioning method is proposed. Compared to existing L1-band-based snapshot positioning method, this approach enhances receiver positioning accuracy and sensitivity across various environments, including challenging scenarios such as urban canyons. The proposed method is based on a two step framework that optimizes the acquisition search range for L5 band signals while maintaining computational efficiency comparable to existing methods. Experiments are conducted using intermediate frequency (IF) signals collected in real-world scenarios. Compared to L1 band snapshot positioning, the proposed method reduces the positioning error (CEP95) by 81.2% and 72.5% in coastal and urban environments, respectively. Furthermore, the proposed two step framework reduces the code phase search range by 92.71% compared to conventional L5 band snapshot positioning.

Global navigation satellite system (GNSS) positioning can be significantly degraded due to multipath and non-line-of-sight (NLOS) signals in urban areas. Cellular vehicle-to-everything (C-V2X) technology provides new opportunities to enhance GNSS performance from a single intelligent vehicle by leveraging roadside GNSS (RSG) and C-V2X. Inspired by this, we propose an RSG-aided GNSS/LiDAR/IMU (RSG-GLIO) method to achieve reliable odometry and mapping, which leverages the high-quality double-differenced (DD) measurements provided by nearby RSG, effectively mitigating shared random errors such as multipath and NLOS. Our RSG-GLIO first estimates the absolute state of the vehicle using onboard sensors. Utilizing this initial positioning estimate, the proposed method introduces a coarse-to-fine selection scheme to identify consistent DD observations from available RSG measurements. Finally, the consistent roadside DD constraints are jointly optimized into factor graph optimization (FGO). Static and dynamic data are extensively evaluated using multiple RSG receivers deployed in the Hong Kong C-V2X testbed to evaluate the effectiveness of roadside-aided positioning. The results demonstrate a significant 36.6% improvement in terms of absolute positioning accuracy compared to the state-of-the-art GLIO method. Furthermore, we showcase the potential for employing RSG as low-cost base stations in dense urban areas. The data of our work is publicly accessible at https://github.com/DarrenWong/RSG-GLIO.

Global navigation satellite system (GNSS) positioning in urban environments suffers from significant accuracy degradation due to non-line-of-sight (NLOS) signal receptions. Existing correction methods, such as 3D model-aided and 3D LiDAR-aided GNSS, lack signal direction information and typically construct candidate reflection paths by exhaustively searching over possible reflection surfaces or azimuth angles, and selecting the final path based on the shortest-path assumption. However, this assumption is often invalid in dense urban canyons. To address this limitation, we propose a novel GNSS NLOS correction method that uses Doppler shift measurements to infer signal directional information, which is integrated with real-time point cloud mapping to reconstruct the actual signal reflection path actively. This approach allows us to directly track signal reflection, eliminating the need for exhaustive candidate generation and the shortest-path assumption. Experiments conducted on datasets collected in urban canyons demonstrate the effectiveness of the proposed method. Results show that the method achieves over 90% correction availability for NLOS signals, leading to more than 50% improvement in 3D GNSS positioning accuracy.

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 and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as Autonomous ground vehicles (AGVs) and autonomous aerial vehicles (AAVs). The visual odometry-based method can provide accurate positioning in a short period but is subject to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation, which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite system (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected by AAV and AGV in indoor and outdoor environments, respectively. The open-source code is available at https://github.com/ZHENGXi-git/SafetyQuantifiable-PLVINS

Pedestrian location tracking in emergency responses and environmental surveys of indoor scenarios tend to rely only on their own mobile devices, reducing the usage of external services. Low-cost and small-sized inertial measurement units (IMU) have been widely distributed in mobile devices. However, they suffer from high-level noises, leading to drift in position estimation over time. In this work, we present a graph-based indoor 3D pedestrian location tracking with inertial-only perception. The proposed method uses onboard inertial sensors in mobile devices alone for pedestrian state estimation in a simultaneous localization and mapping (SLAM) mode. It starts with a deep vertical odometry-aided 3D pedestrian dead reckoning (PDR) to predict the position in 3D space. Environment-induced behaviors, such as corner-turning and stair-taking, are regarded as landmarks. Multi-hypothesis loop closures are formed using statistical methods to handle ambiguous data association. A factor graph optimization fuses 3D PDR and behavior loop closures for state estimation. Experiments in different scenarios are performed using a smartphone to evaluate the performance of the proposed method, which can achieve better location tracking than current learning-based and filtering-based methods. Moreover, the proposed method is also discussed in different aspects, including the accuracy of offline optimization and proposed height regression, and the reliability of the multi-hypothesis behavior loop closures. The video (YouTube) or (BiliBili) is also shared to display our research.

We propose a feasibility study for real-time automated data standardization leveraging Large Language Models (LLMs) to enhance seamless positioning systems in IoT environments. By integrating and standardizing heterogeneous sensor data from smartphones, IoT devices, and dedicated systems such as Ultra-Wideband (UWB), our study ensures data compatibility and improves positioning accuracy using the Extended Kalman Filter (EKF). The core components include the Intelligent Data Standardization Module (IDSM), which employs a fine-tuned LLM to convert varied sensor data into a standardized format, and the Transformation Rule Generation Module (TRGM), which automates the creation of transformation rules and scripts for ongoing data standardization. Evaluated in real-time environments, our study demonstrates adaptability and scalability, enhancing operational efficiency and accuracy in seamless navigation. This study underscores the potential of advanced LLMs in overcoming sensor data integration complexities, paving the way for more scalable and precise IoT navigation solutions.

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.

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.

Smooth and accurate global navigation satellite system (GNSS) positioning for pedestrians in urban canyons is still a challenge due to the multipath effects and the non-line-of-sight (NLOS) receptions caused by the reflections from surrounding buildings. Factor graph optimization (FGO) attracts more and more attention in GNSS society for improving urban GNSS positioning by effectively exploiting the measurement redundancy from historical information to resist the outlier measurements. Unfortunately, the FGO-based GNSS standalone positioning is still challenged in highly urbanized areas. As an extension of the previous FGO-based GNSS positioning method, the potential of the pedestrian dead reckoning (PDR) model in FGO to improve the GNSS standalone positioning performance in urban canyons is exploited in this paper. Specifically, the relative motion of the pedestrian is estimated based on the raw acceleration measurements from the onboard smartphone inertial measurement unit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Doppler measurements, and relative motion from PDR are integrated using the FGO. Given the context of pedestrian navigation with a small acceleration most of the time, a novel soft motion model is proposed to smooth the states involved in the factor graph model. This paper verified the effectiveness of employing the PDR model in FGO step-by-step through two datasets collected in dense urban canyons of Hong Kong using smartphone-level GNSS receivers. The comparison between the conventional extended Kalman filter, several existing methods, and FGO-based integration is presented. The proposed method shows better results than the conventional FGO method in all test datasets, with at least a 22% decrease in the mean value of positioning error. The proposed method reduces the average localization error from 31.64 m to 18.51 m in a deep urban 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.

Indoor localization by smartphones has indicated its promising application prospect in daily life. Smartphone-based pedestrian dead reckoning (PDR) is a common method to obtain the locations. However, PDR suffers from position error accumulation. Although radio frequency (RF) and indoor map can be utilized to restrain the error drift, it requires the prior deployment of facilities or information, which is unsuitable for unknown environments. This paper proposes a factor graph optimization (FGO)-based indoor pedestrian simultaneous localization and mapping (SLAM) with probabilistic exact activity loop closures using a smartphone. In this paper, the smartphone built-in inertial measurement unit (IMU) is solely used to achieve SLAM, in which the human turning activity is regarded as the landmark. Repeatedly observed activities are then used to form loop closures to restrain the drift. FGO is first utilized to formulate pedestrian IMU-only SLAM, which achieves better estimation accuracy than the filter-based method. Moreover, multi-hypothesis tracking is employed to deal with ambiguous data association. During the turning, key points are defined and mutually matched to form exact loop closures to improve estimation accuracy. Simulations and experimental tests are both done to evaluate the performance of the proposed method.

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.

To improve the robustness and reliability of multi-sensor navigation and reduce the uncertainties and complexity of sensor management in challenging environment, a resilient interactive sensor-independent-update (ISIU) method is proposed. Inspired by the interactive cooperation theory, the contributions can be divided into two aspects. Firstly the priority of trust of navigation sensors is introduced into the information fusion in the form of transition probability matrix defined by Markov chain. Secondly every observable sensor is integrated with the propagated system in an elemental filter with sensor-independent-update structure. The multi-sensor integration is implemented in state estimation domain enhanced by interactive information fusion rather than in measurement domain implemented in traditional filter method. The overall estimation is determined by the weighted sum of average from every filter estimate. This weight of every model is dynamic updated by the prior transition information and posterior model likelihood. The same independent structure is also applied to adopt new available sensor to realize plug-and-play navigation. The kinematic vehicle experiment in sub-urban and urban canyon environment verified the superiority of the proposed method. The ISIU method shows better accuracy and reliability compared to classical Kalman filters. The introduction of priority of sensors and decoupled measurement update process make it robust and insensitive to sensor measurement noise and outliers. The interactive sensor-independent-update structure has the natural function of fault detection and exclusion without additional operations. The effect of dynamic sensor selection is achieved in this processing. The proposed ISIU method is pretty suitable for resilient navigation in challenging environments.

This paper proposes a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method due to both static buildings and dynamic objects. A sliding window map describing the environment of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR sensor. Subsequently, the NLOS receptions are detected based on the sliding window map using a proposed quick searching method which eliminates the reliance on the initial guessing of the position of the GNSS receiver. Instead of directly excluding the detected NLOS satellites from further estimating the position, this paper rectifies the pseudo-range measurement model by (1) correcting the pseudo-range measurements if the reflecting point of the NLOS signals is detected within the sliding window map, and (2) remodeling the uncertainty in the NLOS pseudo-range measurement using a novel weighting scheme. The performance of the proposed model was experimentally evaluated in several typical urban canyons in Hong Kong using an automobile-level GNSS receiver. Furthermore, the potential of the proposed NLOS mitigation method in GNSS and the integration of inertial navigation systems were evaluated via factor graph optimization.

Global navigation satellite systems (GNSS) are one of the utterly popular sources for providing globally referenced positioning for autonomous systems. However, the performance of the GNSS positioning is significantly challenged in urban canyons, due to the signal reflection and blockage from buildings. Given the fact that the GNSS measurements are highly environmentally dependent and time-correlated, the conventional filtering-based method for GNSS positioning cannot simultaneously explore the time-correlation among historical measurements. As a result, the filtering-based estimator is sensitive to unexpected outlier measurements. In this paper, we present a factor graph-based formulation for GNSS positioning and real-time kinematic (RTK). The formulated factor graph framework effectively explores the time-correlation of pseudorange, carrier-phase, and doppler measurements, and leads to the non-minimal state estimation of the GNSS receiver. The feasibility of the proposed method is evaluated using datasets collected in challenging urban canyons of Hong Kong and significantly improved positioning accuracy is obtained, compared with the filtering-based estimator.

With the rise of digital twins and smart cities, Building Information Modelling have been widely adopted by the construction industry from design, construction to operation & maintenance. We present a BIPS (Building Information Positioning System) method which integrates a smartphone VPS (visual positioning system) based on the BIM models, and VO (visual odometry) for the indoor positioning. Firstly, the smartphone images and sensor measurements are sent to a server. In the server, the VPS utilizes computer vision algorithms to extract semantics from the smartphone images. Then, the smartphone image semantics are compared with the BIM semantics. The hypothesized position candidates are distributed in the BIM model. The candidate with the maximum likelihood is regarded as the VPS heading and position estimation. An extended Kalman filter is then used to integrate the VPS with VO, where the former and latter provide measurement and propagation models, respectively. According to the simulation result, the proposed BIPS proves effective in an indoor environment, being capable of improving indoor positioning accuracy to about 1 meter.

The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

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.

Self-localization is one of the most important part in autonomous driving system. In urban canyon, the multipath and non-line-of-sight effects to GPS receiver decrease the precision of self-localization of the vehicle. More specifically, the lateral error is more serious because of the blockage of the satellites. However, the building on roadside could be the stable reference object for localization. Therefore, this paper proposes to use stereo camera and 3D building map to reduce the lateral error of positioning result. In our proposal, stereo camera is used to detect and reconstruct the building side view. Lateral distance between building and vehicle estimated by stereo camera is compared with 3D building map to rectify the lateral position of vehicle. In addition, this paper employs inertial sensor and GPS receiver to decide the longitudinal position of vehicle. The particle filter is used for the sensor fusion. The experiment is conducted in the center of Tokyo, Japan, which is a typical urban city scene with high density of tall buildings. It demonstrates that the proposed method could achieve sub-meter level accuracy in GPS difficult environments.

The accuracy of the positions of a pedestrian is very important and useful information for the statistics, advertisement, and safety of different applications. Although the GPS chip in a smartphone is currently the most convenient device to obtain the positions, it still suffers from the effect of multipath and nonline-of-sight propagation in urban canyons. These reflections could greatly degrade the performance of a GPS receiver. This paper describes an approach to estimate a pedestrian position by the aid of a 3-D map and a ray-tracing method. The proposed approach first distributes the numbers of position candidates around a reference position. The weighting of the position candidates is evaluated based on the similarity between the simulated pseudorange and the observed pseudorange. Simulated pseudoranges are calculated using a ray-tracing simulation and a 3-D map. Finally, the proposed method was verified through field experiments in an urban canyon in Tokyo. According to the results, the proposed approach successfully estimates the reflection and direct paths so that the estimate appears very close to the ground truth, whereas the result of a commercial GPS receiver is far from the ground truth. The results show that the proposed method has a smaller error distance than the conventional method.