Abstract
The location of entities in a smart indoor environments is an important context information. To this end, several indoor localization algorithm have been proposed with the received signal strength fingerprint (RSS-F) based algorithm being the most attractive due to the higher localization accuracy. However, RSS-F based localization accuracy is highly degraded on account of non-line-of-sight (NLOS) propagation in indoor or harsh environment. This thesis proposes an approach for NLOS self-monitoring and autonomous compensation. Firstly, the localization area is regionalized according to Voronoi Diagram. Then, the self-monitoring and autonomous compensation is realized by propagation environment similarity represented by the dynamic path attenuation index between the domains. The verification experiment results show that the proposed algorithms can adaptively identify the NLOS interference and accomplish compensation. Compared with other localization algorithm, the maximum error is reduced from 3.04 m to 1.71 m, the average error is reduced to 0.90 m, and the localization time is reduced to 2.113 s (contain 10 test point) compared with other tracking algorithm.
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Acknowledgment
This work was financially supported by the Science and Technology Commission of Shanghai Municipality of China under Grant (No. 17511107002).
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Li, A., Fu, J., Shen, H. (2017). Research on Indoor Fingerprint Localization System Based on Voronoi Segmentation. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_30
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DOI: https://doi.org/10.1007/978-981-10-6370-1_30
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