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基于信道状态信息的序列式室内定位

Sequence-Based Indoor Localization with Channel Status Information.

机构信息

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

出版信息

Sensors (Basel). 2018 Jun 4;18(6):1818. doi: 10.3390/s18061818.

DOI:10.3390/s18061818
PMID:29867056
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6022130/
Abstract

Most of the indoor localization systems nowadays are based on received signal strength indication (RSSI), which has further increased the importance of precise localization of access points (AP) in a wireless local area network (WLAN). Since most existing AP localization algorithms suffer from a high error rate in practical scenarios due to multipath fading and temporal dynamics, we propose an AP localization algorithm based on the channel status information (CSI) sequence-based localization (SBL-CSI). The proposed algorithm SBL-CSI is an efficient localization method that consists of the following three major steps: Firstly, a 2D localization space is divided by special APs into distinct regions, and each region has a unique location sequence that represents the distance ranks of special APs to that region and constructs the location sequence table. Then, the relative distance of the ordinary AP, served in the location sequence, is obtained by using CSI between the ordinary AP and special AP. Finally, the "nearest" feasible sequence of the ordinary AP in the location sequence table is searched, and the centroid of the corresponding region is the ordinary AP's localization. Compared with the traditional localization algorithm based on RSSI, the experiment results demonstrate that the positioning accuracy is improved approximately 24.31%.

摘要

目前,大多数室内定位系统都基于接收信号强度指示 (RSSI),这进一步增加了无线局域网 (WLAN) 中接入点 (AP) 精确定位的重要性。由于大多数现有的 AP 定位算法由于多径衰落和时间动态性而在实际场景中存在高误差率,因此我们提出了一种基于信道状态信息 (CSI) 序列的 AP 定位算法 (SBL-CSI)。所提出的 SBL-CSI 算法是一种有效的定位方法,它由以下三个主要步骤组成:首先,通过特殊的 AP 将 2D 定位空间划分为不同的区域,每个区域都有一个唯一的位置序列,表示特殊 AP 到该区域的距离等级,并构建位置序列表。然后,通过普通 AP 和特殊 AP 之间的 CSI 获得普通 AP 的相对距离。最后,在位置序列表中搜索普通 AP 的“最近”可行序列,相应区域的质心就是普通 AP 的定位。与传统的基于 RSSI 的定位算法相比,实验结果表明定位精度提高了约 24.31%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/cc618f637843/sensors-18-01818-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/6cd4345048cf/sensors-18-01818-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/f93a58323778/sensors-18-01818-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/e54a4a4dd137/sensors-18-01818-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/7ba2e281f208/sensors-18-01818-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/f0477e35ee72/sensors-18-01818-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/b7b2c7d3f9f5/sensors-18-01818-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/636d8767c6a8/sensors-18-01818-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/0a3ca0291a41/sensors-18-01818-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/f27d59f21d24/sensors-18-01818-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/cc618f637843/sensors-18-01818-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/6cd4345048cf/sensors-18-01818-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/f93a58323778/sensors-18-01818-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/e54a4a4dd137/sensors-18-01818-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/7ba2e281f208/sensors-18-01818-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/f0477e35ee72/sensors-18-01818-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/b7b2c7d3f9f5/sensors-18-01818-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/636d8767c6a8/sensors-18-01818-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/0a3ca0291a41/sensors-18-01818-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/f27d59f21d24/sensors-18-01818-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a54b/6022130/cc618f637843/sensors-18-01818-g010.jpg

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Sensors (Basel). 2018 Jan 4;18(1):126. doi: 10.3390/s18010126.
2
A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering.一种具有信道分集、加权三边测量和卡尔曼滤波的低功耗蓝牙室内定位系统。
Sensors (Basel). 2017 Dec 16;17(12):2927. doi: 10.3390/s17122927.
3
An Indoor Location-Based Control System Using Bluetooth Beacons for IoT Systems.
一种用于物联网系统的基于蓝牙信标的室内定位控制系统。
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4
A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering.一种基于具有特征提取和聚类的WLAN位置指纹的智能手机室内定位算法。
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5
Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting.基于不变Wi-Fi接收信号强度指纹识别的室内定位传感
Sensors (Basel). 2016 Nov 11;16(11):1898. doi: 10.3390/s16111898.