The reduction of multipath errors is a significant challenge in the Global Navigation Satellite System (GNSS), especially when receiving non-line-of-sight (NLOS) signals. However, selecting line-of-sight (LOS) satellites correctly is still a difficult task in dense urban areas, even with the latest GNSS receivers. This study demonstrates a new method of utilization of C/N of the GNSS to detect NLOS signals. The elevation-dependent threshold of the C/N setting may be effective in mitigating multipath errors. However, the C/N fluctuation affected by NLOS signals is quite large. If the C/N is over the threshold, the satellite is used for positioning even if it is still affected by the NLOS signal, which causes the positioning error to jump easily. To overcome this issue, we focused on the value of continuous time-series C/N for a certain period. If the C/N of the satellite was less than the determined threshold, the satellite was not used for positioning for a certain period, even if the C/N recovered over the threshold. Three static tests were conducted at challenging locations near high-rise buildings in Tokyo. The results proved that our method could substantially mitigate multipath errors in differential GNSS by appropriately removing the NLOS signals. Therefore, the performance of real-time kinematic GNSS was significantly improved.
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http://dx.doi.org/10.3390/s20144059 | DOI Listing |
Data Brief
December 2024
Faculty of Information Science and Technology, Multimedia University Melaka Campus, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia.
This study presents the "ESP32 Dataset," a dataset of radio frequency (RF) data intended for human activity detection. This dataset comprises 10 activities carried out by 8 volunteers in three different indoor floor plan experiment setups. Line-of-sight (LOS) scenarios are represented by the first two experiment setups, and non-line-of-sight (NLOS) scenarios are simulated in the third experiment setup.
View Article and Find Full Text PDFIn this paper, a semantic communication-based scheme was proposed to tackle the optimization challenge of transmission efficiency and link stability in indoor visible light communication (VLC) systems utilizing light-emitting diodes for image transmission. The semantic model, established by deep convolutional generative adversarial network (DCGAN) and vector quantization method, can effectively extract the essential characteristics of images. In addition, indoor VLC channel models including line-of-sight (LOS) and non-line-of-sight (NLOS) links are established in a 5*5*3 room, while incorporating noise interference encountered during signal transmission into the training process of the semantic model to enhance its anti-interference capability.
View Article and Find Full Text PDFWith the rapid development of the Internet of Things, location-based services are becoming increasingly important, especially in indoor environments. Visible light positioning (VLP) has garnered widespread attention due to its high accuracy, low cost, and immunity to the radio frequency electromagnetic interference. However, traditional VLP relies on line-of-sight paths, making it impractical in complex and dynamic indoor environments.
View Article and Find Full Text PDFNat Comput Sci
December 2024
Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, University of Science and Technology of China, Hefei, China.
Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes.
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