@techreport{oai:ipsj.ixsq.nii.ac.jp:00227874,
 author = {Heng, Zhou and Takuya, Maekawa and Heng, Zhou and Takuya, Maekawa},
 issue = {47},
 month = {Sep},
 note = {The application of GPS in indoor environments remains a challenge due to the loss of signal precision. This research introduces a novel method for predicting the GPS signal strength received by smart-phones at arbitrary positions within a specified floor of a building. These predicted signal strengths could serve as a simple indoor fingerprinting system, obviating the need for an additional signaling infrastructure. We suggested a neural network-based system for signal strength prediction that mainly depends on the current position of the satellite of interest and the floor plan of the target floor incorporating wall and window information. Furthermore, we employ information about the azimuthal and elevation angles of the GPS satellite, as well as shapes and heights of nearby tall buildings, to estimate the line of sight (LOS) from each satellite to the target environment, which also serves as input. Preliminary experiments of this framework using real-world building data have been conducted., The application of GPS in indoor environments remains a challenge due to the loss of signal precision. This research introduces a novel method for predicting the GPS signal strength received by smart-phones at arbitrary positions within a specified floor of a building. These predicted signal strengths could serve as a simple indoor fingerprinting system, obviating the need for an additional signaling infrastructure. We suggested a neural network-based system for signal strength prediction that mainly depends on the current position of the satellite of interest and the floor plan of the target floor incorporating wall and window information. Furthermore, we employ information about the azimuthal and elevation angles of the GPS satellite, as well as shapes and heights of nearby tall buildings, to estimate the line of sight (LOS) from each satellite to the target environment, which also serves as input. Preliminary experiments of this framework using real-world building data have been conducted.},
 title = {Preliminary Investigation of Indoor GPS Satellite Reception Prediction via Neural Networks},
 year = {2023}
}