Simulation of GNSS Availability in Urban Environments Using a Panoramic Image Dataset

Tongleamnak, Sakpod; Nagai, Masahiko

doi:10.1155/2017/8047158

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…To guarantee GNSS positioning accuracy, NLOS signals must be detected and corrected [9][10][11][12][13][14][15][16][17][18][19][20][21][22] . Scientists and researchers in the community have given much attention to this problem, and various methods have been proposed and investigated for detecting NLOS conditions and correcting the resulting errors.…”

Section: Related Workmentioning

confidence: 99%

“…Correlator outputs are processed by deep learning neural networks (convolutional neural networks, CNNs), and the features are automatically learned [17] . In addition, external information or environmental awareness, i.e., 3D city models, sky-pointing fisheye cameras, and LiDAR, can be utilized to detect NLOS conditions [19][20][21][22] . With a 3D city model or LiDAR data, the elevation angle of the boundary of the surrounding buildings can be extracted and compared to that of the satellites to detect NLOS conditions [19][20][21][22] .…”

Section: Related Workmentioning

confidence: 99%

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Vector Tracking Based on Factor Graph Optimization for GNSS NLOS Bias Estimation and Correction

Jiang

Chen

et al. 2022

IEEE Internet Things J.

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Position and location constitute critical context for IoT (Internet of Things) devices. Global navigation satellite systems (GNSSs) are the primary apparatus providing precise position and location information for IoT devices in outdoor environments. However, in dense urban areas, non-line-of-sight (NLOS) signals will induce large errors in GNSS pseudorange measurements due to the additional signal transmission paths. The vector tracking (VT) technique utilizing a Kalman filter (KF) to estimate navigation solutions has been investigated in NLOS detection, and its advantages have been demonstrated. However, the estimation of NLOS-induced bias has not been thoroughly investigated in the VT framework. In this paper, we focus on the estimation and correction of NLOS-induced errors within the VT framework. First, graph optimization (GO) instead of a KF is incorporated with VT to optimize the estimation of navigation solutions. The NLOS-induced bias is then added to the VT state vector as the variable for real-time estimation. Compared with the KF-VT method, in GO-VT, the state transformation and the measurement model are regarded as constraints to optimize the state vector estimation. Hence, the GO-VT framework is more

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Vector Tracking Based on Factor Graph Optimization for GNSS NLOS Bias Estimation and Correction

Jiang

Chen

et al. 2022

IEEE Internet Things J.

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“…At present, the 5G standard does not prescribe the detailed implementation of WPS in LCS, and we use the Google Maps geolocation service as an alternative solution to acquiring the positioning result. Table 3 shows the WiFi AP list collected (1) Collect all WiFi APs in the environment as the points in the dataset (2) Estimate the Wasserstein metric between each pair of points in the dataset (3) Find the points in the ε-neighborhood of every point, and identify the core points with more than min_pts neighbors (4) Find the connected components of core points on the neighbor graph, ignoring all noncore points (5) Assign each noncore point to a nearby cluster if the cluster is an ε-neighborhood, otherwise assign it to noise (6) Mark the remaining noise points in the dataset as legitimate APs and the ones that have been clustered as fake APs ALGORITHM 1: Density-based spatial clustering of applications with noise.…”

Section: Real World Experimentmentioning

confidence: 99%

“…In the architecture of LCS, GNSS can provide the most accurate position for the user's mobile device in the open area, but it suffers from the poor visibility of satellites in the urban area and high power consumption [2]. On the contrary, WPS can provide fast and relatively less accurate positioning results in the indoor area where other methods are inadequate due to multipath or signal blockage.…”

Section: Introductionmentioning

confidence: 99%

Wasserstein Metric-Based Location Spoofing Attack Detection in WiFi Positioning Systems

Tian

Zheng

Chen

et al. 2021

Security and Communication Networks

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WiFi positioning systems (WPS) have been introduced as parts of 5G location services (LCS) to provide fast positioning results of user devices in urban areas. However, they are prominently threatened by location spoofing attacks. To end this, we present a Wasserstein metric-based attack detection scheme to counter the location spoofing attacks in the WPS. The Wasserstein metric is used to measure the similarity of each two hotspots by their signal’s frequency offset distribution features. Then, we apply the clustering method to find the fake hotspots which are generated by the same device. When applied with WPS, the proposed method can prevent location spoofing by filtering out the fake hotspots set by attackers. We set up experimental tests by commercial WiFi devices, which show that our method can detect fake devices with 99% accuracy. Finally, the real-world test shows our method can effectively secure the positioning results against location spoofing attacks.

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“…The average positioning error in case of PPP is less than one meter in clear open-sky [6]. However, this level of accuracy cannot be achieved in obstructed environments even if the receiver is equipped with additional error modelling [29], because there are significant chances of navigation services being interrupted or their performance reduced due to signal blockage and MP/NLOS [7], [30], [31] leading to inaccurate positioning solution.…”

Section: Introductionmentioning

confidence: 99%

Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments

et al. 2020

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Ubiquitous navigation requires timely, uninterrupted and accurate estimate of receiver's position at all times, in all environments and for all modes of transportation and it is highly dependent on satellite availability, geometry and accurate positioning estimation. However, the availability, continuity and accuracy of a GNSS can be severely affected in a highly dynamic environment due to blockage, fading and multipath. This results in positioning information inaccurate, unreliable and sometimes unavailable. This paper presents a study on the potential vulnerabilities that can affect a multi-constellation multi-frequency GNSS receiver in low to highly dynamic multipath environments such as clear line-of-sight, partially and highly obstructed environments to characterize the distortions/anomalies which could significantly affect the satellite signals and their impact on positioning and navigation. The multi-constellation multi-frequency GNSS receiver configuration in this paper is set to GPS, GLONASS, Galileo and BeiDou for the first time at full capacity and performance comparison is made with the GPS based on satellite availability, blockage, continuity, precision and accuracy parameters. These parameters are then used in this paper to detect and characterize the type of environment for the multi-constellation GNSS receiver without using any external aiding devices or sensors. Based on environment detection and characterization, a new Adaptive Environment Navigation (AEN) based GNSS receiver design is proposed which can work in real time and has achieved an overall availability and accuracy factor of 94% in highly dynamic multipath/NLOS environment along with a reduction in the blockage coefficient, β, by almost 11% resulting in more accuracy and precision than the standard multi-constellation GNSS receiver where the availability factor was found to be 57% only.

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Simulation of GNSS Availability in Urban Environments Using a Panoramic Image Dataset

Cited by 8 publications

References 14 publications

Vector Tracking Based on Factor Graph Optimization for GNSS NLOS Bias Estimation and Correction

Vector Tracking Based on Factor Graph Optimization for GNSS NLOS Bias Estimation and Correction

Wasserstein Metric-Based Location Spoofing Attack Detection in WiFi Positioning Systems

Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments

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