Research on indoor multi-floor positioning method based on LoRa

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
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引用次数: 0

Abstract

Existing floor localization methods are plagued by low accuracy, high algorithmic complexity, dense node deployment, susceptibility to environmental factors, and the inability to track trajectories. This paper introduces a localization method designed to address the challenges of multi-floor environments, leveraging LoRa technology. The approach involves deploying LoRa vertical positioning devices and establishing offline and threshold fingerprint databases. To enhance localization accuracy, it combines Time-of-Flight (TOF) ranging values (referred to as "RANGE" in this paper) with Received Signal Strength Indicator (RSSI) values, referred to as "RSSI-RANGE". Subsequently, a multi-floor determination is achieved using the RSSI-RANGE floor determination algorithm and a range-based signal source autonomous switching mechanism. The fingerprinting technique is then employed for trajectory recognition. Comprehensive vertical information is obtained by combining floor determination and trajectory award. Gaussian filtering is utilized for fingerprint preprocessing to eliminate gross errors. The particle swarm optimization algorithm is employed to fine-tune the hyperparameters of the random forest algorithm following noise reduction. Using the random forest algorithm, optimal RSSI-RANGE values are derived, and the offline fingerprint database is established by applying Kriging interpolation. Localization is then achieved in the concluding online recognition phase. Empirical findings illustrate the system's high floor accuracy rate of 97.8%, achieving high determination accuracy and comprehensive floor localization when combined with trajectory recognition.
基于 LoRa 的室内多楼层定位方法研究
现有的楼层定位方法存在精度低、算法复杂度高、节点部署密集、易受环境因素影响以及无法跟踪轨迹等问题。本文介绍了一种利用 LoRa 技术应对多楼层环境挑战的定位方法。该方法涉及部署 LoRa 垂直定位设备,并建立离线和阈值指纹数据库。为提高定位精度,该方法将飞行时间(TOF)测距值(本文中称为 "RANGE")与接收信号强度指示器(RSSI)值相结合,称为 "RSSI-RANGE"。随后,利用 RSSI-RANGE 底限确定算法和基于范围的信号源自主切换机制实现多底限确定。然后采用指纹识别技术进行轨迹识别。通过结合楼层确定和轨迹判定,可获得全面的垂直信息。指纹预处理采用高斯滤波,以消除严重错误。降噪后,采用粒子群优化算法对随机森林算法的超参数进行微调。利用随机森林算法得出最佳 RSSI-RANGE 值,并通过克里金插值法建立离线指纹数据库。然后在最后的在线识别阶段实现定位。实证结果表明,该系统的楼层准确率高达 97.8%,与轨迹识别相结合,实现了较高的确定准确率和全面的楼层定位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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