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实时递归指纹无线电地图创建算法,结合 Wi-Fi 和地磁。

Real-Time Recursive Fingerprint Radio Map Creation Algorithm Combining Wi-Fi and Geomagnetism.

机构信息

Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University, #727 Taejong-Ro, Youngdo-Gu, Busan 49112, Korea.

Division of Electronics and Electrical Information Engineering, Korea Maritime and Ocean University, #727 Taejong-Ro, Youngdo-Gu, Busan 49112, Korea.

出版信息

Sensors (Basel). 2018 Oct 10;18(10):3390. doi: 10.3390/s18103390.

DOI:10.3390/s18103390
PMID:30309033
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6210187/
Abstract

Fingerprint is a typical indoor-positioning algorithm, which measures the strength of wireless signals and creates a radio map. Using this radio map, the position is estimated through comparisons with the received signal strength measured in real-time. The radio map has a direct effect on the positioning performance; therefore, it should be designed accurately and managed efficiently, according to the type of wireless signal, amount of space, and wireless-signal density. This paper proposes a real-time recursive radio map creation algorithm that combines Wi-Fi and geomagnetism. The proposed method automatically recreates the radio map using geomagnetic radio-map dual processing (GRDP), which reduces the time required to create it. It also reduces the size of the radio map by actively optimizing its dimensions using an entropy-based minimum description length principle (MDLP) method. Experimental results in an actual building show that the proposed system exhibits similar map creation time as a system using a Wi-Fi⁻based radio map. Geomagnetic radio maps exhibiting over 80% positioning accuracy were created, and the dimensions of the radio map that combined the two signals were found to be reduced by 23.81%, compared to the initially prepared radio map. The dimensions vary according to the wireless signal state, and are automatically reduced in different environments.

摘要

指纹是一种典型的室内定位算法,它通过测量无线信号的强度来创建无线电地图。使用该无线电地图,通过与实时测量的接收信号强度进行比较来估计位置。无线电地图对定位性能有直接影响;因此,应根据无线信号类型、空间大小和无线信号密度,准确地设计并有效地管理无线电地图。本文提出了一种实时递归无线电地图创建算法,该算法结合了 Wi-Fi 和地磁。所提出的方法使用地磁无线电地图双重处理(GRDP)自动重新创建无线电地图,从而减少了创建所需的时间。它还通过使用基于熵的最小描述长度原理(MDLP)方法主动优化其维度来减小无线电地图的大小。在实际建筑物中的实验结果表明,所提出的系统在地图创建时间上与使用 Wi-Fi 无线电地图的系统相似。创建了地磁无线电地图,其定位精度超过 80%,并且与最初准备的无线电地图相比,结合两种信号的无线电地图的维度减少了 23.81%。维度根据无线信号状态而变化,并在不同环境中自动减小。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/76b33bf987a7/sensors-18-03390-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/bd4f8ce9f0c1/sensors-18-03390-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/0b5eda8a61aa/sensors-18-03390-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/5c2347200582/sensors-18-03390-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/ae973e309ffd/sensors-18-03390-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/40210da3e6a7/sensors-18-03390-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/9eef5d3c8396/sensors-18-03390-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/bd9362e3c35f/sensors-18-03390-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/db08919808c8/sensors-18-03390-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/6fce1d385378/sensors-18-03390-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/76b33bf987a7/sensors-18-03390-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/bd4f8ce9f0c1/sensors-18-03390-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/0b5eda8a61aa/sensors-18-03390-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/5c2347200582/sensors-18-03390-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/ae973e309ffd/sensors-18-03390-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/40210da3e6a7/sensors-18-03390-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/9eef5d3c8396/sensors-18-03390-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/bd9362e3c35f/sensors-18-03390-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/db08919808c8/sensors-18-03390-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/6fce1d385378/sensors-18-03390-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b0/6210187/76b33bf987a7/sensors-18-03390-g010.jpg

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Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks.基于无线传感器网络中德劳内三角剖分和启发式信息的室内轨迹跟踪方案
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An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study.基于卡尔曼融合的改进型蓝牙低功耗室内定位:一项实验研究。
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