Simulation of GPS localisation based on ray tracing

Friebel, Björn; Schweins, Michael; Dreyer, Nils; Kürner, Thomas

doi:10.5194/ars-19-85-2021

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Hence, they are prone to deviate a lot from practical propagation. According to the interactions between electromagnetic waves and urban geographical objects, ray-tracing model [29] is able to track hundreds of propagation paths for each receiving sensor but is susceptible to small errors in geographic models. Considering geographic information, Wahl et al [30] propose dominant path model (DPM).…”

Section: The Urban Propagation Model With Geographicmentioning

confidence: 99%

Deep Learning-Based Localization with Urban Electromagnetic and Geographic Information

Wang

Huang

et al. 2022

Wireless Communications and Mobile Computing

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There is a growing demand for localization of illegal signal sources, aiming to guarantee the security of urban electromagnetic environment. The performance of traditional localization methods is limited due to the non-line-of-sight (NLOS) propagation and sparse layouts of sensors. In this paper, a deep learning-based localization method is proposed to overcome these issues in urban scenarios. Firstly, a model of electromagnetic wave propagation considered with geographic information is proposed to prepare reliable datasets for intelligent cognition of urban electromagnetic environment. Then, this paper improves an hourglass neural network which consists of downsampling and upsampling layers to learn the propagation features from sensing data. The core modules of VGG and ResNet are, respectively, utilized as feature extractors in downsampling. Moreover, this paper proposes a weighted loss function to expand the attention on position features, in order to improve the performance of localization with sparse layouts of sensors. Representative numerical results are discussed to assess the proposed method. ResNet-based extractor performs more efficiently than VGG-based extractor, and the proposed weighted loss function increases the localization accuracy by more than 50%. Additionally, the established geographic model supports qualitative and quantitative evaluation of the robustness with varied degree of NLOS propagation. Compared with other deep learning-based algorithms, the proposed method presents the more robust and superior performance under severe NLOS propagation and sparse sensing conditions.

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Section: The Urban Propagation Model With Geographicmentioning

confidence: 99%

Deep Learning-Based Localization with Urban Electromagnetic and Geographic Information

Wang

Huang

et al. 2022

Wireless Communications and Mobile Computing

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Predicting Signal Reception Information from GNSS Satellites in Indoor Environments without Site Survey: Towards Opportunistic Indoor Positioning Based on GNSS Fingerprinting

Zhou,

Maekawa

2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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While traditional GPS localization is impractical in certain indoor environments, GPS satellites offer a valuable source of indoor contextual information, particularly around window-side areas where signals are more accessible. This study introduces a new method that predicts GPS signal reception information in a target indoor environment without the need for actual data collection in the target environment. By predicting the strengths of GPS signals that are expected to be measured at various indoor positions, we can construct a GPS signal reception map of a target environment, which functions similarly to a Wi-Fi fingerprinting map surrounding window-side areas, offering a new dimension in indoor positioning systems. For instance, it aids in the correction of accumulated errors in pedestrian dead reckoning (PDR) systems. Our proposed method leverages easily accessible inputs, including satellite location data, indoor floorplan image of the target floor, and 3D mapping of the surrounding buildings, to predict signal reception at each position in the target environment. We developed a specialized neural network, named multi-scale branch fusion network (MSBF-Net), designed to process and integrate data of varying scales, such as the floorplan image of the target environment and 3D map of the surrounding area obtained from Google Earth, with a specific focus on understanding the line-of-sight (LOS) and multi-path effects caused by internal obstacles and surrounding buildings. This advanced capability enables the network to effectively interpret complex signal interactions within urban environments, enhancing its predictive accuracy for GPS signal reception. The effectiveness of our method was rigorously evaluated using real-world environments. In addition, we employed our method to implement opportunistic GPS fingerprint-based indoor positioning, where position estimates are provided when a strong signal is observed from at least one satellite. Surprisingly, the positioning method achieved a positioning error of only 2.8 meters when a smartphone is close to window-side areas even though the method does not rely on labeled training data collected in a target environment and signal infrastructures installed in the target environment.

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Simulation of GPS localisation based on ray tracing

Cited by 2 publications

References 13 publications

Deep Learning-Based Localization with Urban Electromagnetic and Geographic Information

Deep Learning-Based Localization with Urban Electromagnetic and Geographic Information

Predicting Signal Reception Information from GNSS Satellites in Indoor Environments without Site Survey: Towards Opportunistic Indoor Positioning Based on GNSS Fingerprinting

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