Abstract
Indoor localization using RSS fingerprint database has become one of the most influential and practical solutions for various types of location service systems, which circumvents proprietary requirements, such as infrastructures, signal transceivers. In order to provide accurate location information, how-ever, a full-scale fingerprint map with high density has to be construct in advance. Furthermore, the established map is obliged to be updated regularly due to instability of RSS or damage of access points. And yet, sampling RSS of abundant reference points (RPs) is undoubtedly an extreme time-consuming and labor-intensive. The interpolation is naturally introduced to lessen the number of samples through collecting partial RPs. This paradigm generally needs to sample adequate RPs for the desirable accuracy in actual surroundings, especially in large-scale scenes. To reduce sampling with along with high-precision, we propose an iterative filling approach for incomplete fingerprint map based on multi-directional signal propagation, called FMS to find missing samplings. Different from the current interpolating concept, FMS do not potentially assume that the missing samples depend on their annular near neighbors, but considers the character of signal propagation to better fitting with the reality. For restraining unfavorable effects caused by reflection, diffraction and re-fraction of signals, FMS mingles multiply direction chains of missing RPs through estimating the RSS value and the weight of each chain. In our experiments, the sampling rates are from 40% to 80%. Extensive experiments demonstrate that FMS has a good stability in various of conditions, including sampling rates and devices. It exceeds 11.35% ~ 24.43% compared to the whole sampling mode, and 14.48% ~ 24.75% compared to the best accuracy of current mainstream methods.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under Grant No. 61772448, Natural Science Foundation of Jiangsu Province under Grant No. BK20191481, Natural Science Major Project of the Higher Education Institutions of Jiangsu Province of China under Grant No. 17KJA520006, China Postdoctoral Science Foundation funded project under Grant No. 2019M660132, Jiangsu Province Postdoctoral Research Subsidy Program under Grant No. 2019K123, Project on the Industry-University-Research Cooperation of Jiangsu Province under Grant No. BY2021326, Scientific Research Subsidy of Jiangsu Province under Grant No. BRA2019288, Future Network Scientific Research Fund Project Grant No. FNSRFP-2021-YB-45, Jiangsu Provincial Key Constructive Laboratory for Big Data of Psychology under Grant No. 72591962002G and 72592162003G, Open Foundation of Jiangsu Provincial Key Laboratory of Coastal Wetland Bioresources and Environmental Protection under Grant No. JLCBE14008.
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Zhao, Y., Ji, Y., Zhu, L., Yang, H. (2022). Iterative Filling Incomplete Fingerprint Map Based on Multi-directional Signal Propagation in Large-Scale Scene. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_20
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DOI: https://doi.org/10.1007/978-3-030-95384-3_20
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