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Improved KNN Algorithm with Historical Information Fusion for Indoor Positioning

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Hui Zhang; Zhikun Wang; Yiyang Ni; Wenchao Xia; Haitao Zhao
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Abstract:

More diverse applications and services pose a high demand for tracking services in indoor environments to improve user experience. Different from other positioning method...Show More

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Abstract:

More diverse applications and services pose a high demand for tracking services in indoor environments to improve user experience. Different from other positioning methods, the trajectory-based positioning system utilizes abundant historical information to further improve positioning accuracy. To better utilize historical information, we propose a novel historical information fusion method based on trajectory for indoor localization. Specifically, we first evaluate the distances between the reference points (RPs) and the previous position to match proper RPs. Then, a fusion weight is calculated according to the previous position and the change tendency of received signal strength. Based on the fusion weight, the position of target node can be determined. Finally, experiments are conducted and simulation results show that the positioning accuracy is improved significantly by the proposed algorithm.
Published in: 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE)
Date of Conference: 17-19 December 2021
Date Added to IEEE Xplore: 18 January 2022
ISBN Information:
DOI: 10.1109/ICECE54449.2021.9674404
Conference Location: Xi'an, China

Funding Agency:

References is not available for this document.
Contents

I. Introduction

More and more demands for indoor localization occur in recent years with the developments of applications such as guidance, rescue operation, etc. The Global Navigation Satellite System (GNSS), such as Global Positioning System, Beidou, as well as Galileo Satellite Navigation System can provide reliable positioning services for outdoor scenarios. However, the positioning performance of GNSS is degraded in the indoor scenarios due to the obstruction of buildings and multi-path effects. Therefore, it is necessary to further investigate positioning algorithms for indoor scenarios.

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