登录
注册
E-alert
登录
注册
E-alert
Construction and evaluation of a Wi-Fi RSSI indoor positioning system in a waste incineration facility
-
摘要: 以提升垃圾焚烧厂作业人员安全管理水平为目标，重点研究了基于室内定位技术的垃圾焚烧厂作业人员位置信息的确定方法，通过利用厂区现有的Wi-Fi接入点，采用FP（finger print：指纹识别）技术特征，在运行中的垃圾焚烧设施中构建了一套低成本、高实用性的作业人员定位系统。在系统建设方面，研究团队创新性地应用了自行研发的指纹高速采集方法，完成了垃圾焚烧厂数据地图的绘制，并基于机器学习算法开发了平均定位误差仅为2.78 m的定位模型。将定位模型应用于实际垃圾焚烧厂进行实时位置验证，结果表明，该系统能够以30 s/次的更新频率，实现平均误差约4 m的定位精度，在节省基础设施安装成本的前提下，可基本满足垃圾焚烧厂安全管理对作业人员实时位置显示精度的需求。Abstract: To enhance worker safety management in waste incineration facilities， we examined indoor positioning methods to track worker locations. We focused on fingerprint （FP） using Wi-Fi， which allows us to utilize existing access points and thus reduce infrastructure deployment costs. We developed a system to track worker positions in an operational waste incineration facility. For system construction， we employed a high-speed fingerprint collection method developed by us to create a data map of the facility. By using a machine learning model， we built a positioning model with an average error of 2.78 meters. Applying the constructed positioning model to an actual waste incineration facility for real-time location display verification， we confirmed that it could provide position displays with an average error of approximately 4 meters at a frequency of once every 30 seconds. This achieved real-time location display of workers with a practical level of accuracy.
-
Key words:
- waste incineration facility /
- safety management /
- indoor positioning /
- fingerprinting /
- Wi-Fi FP /
- machine learning
-
[1] ANJASMARA I M，PRATOMO D G，RISTANTO W，et al. Accuracy analysis of GNSS（GPS，GLONASS，and Beidou）observation for positioning［C］// E3S Web of Conferences，EDP Sciences，2019:01019. [2] NDZUKULA S，RAMOTSOELA T，SILVA B，et al. A Bluetooth low energy based system for personnel tracking［C］// IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society，IEEE，2017:8435-8440. [3] SAKAMOTO Y，EBINUMA T，FUJII K. Doppler positioning with a movable receiver antenna for high-accuracy IMES localization［J］. SICE Journal of Control，Measurement，and System Integration，2012，5（5）:259-267. [4] LI Z，LIU J，WANG Z，et al. A novel fingerprinting method of WiFi indoor positioning based on Weibull Signal Model［C］// China Satellite Navigation Conference（CSNC）2018 Proceedings，Springer，Singapore，2018. [5] TAO Y，ZHAO L. AIPS:An accurate indoor positioning system with fingerprint map adaptation［J］. IEEE Internet Things J，2022，9（4）:3062-3073. [6] YANG J，WANG Y，CHENG W，et al. A fingerprint dictionary processing approach in indoor localization system based on Wi-Fi［J］. Sci Rep，2024（14）:24944. [7] https://www.city.otsu.lg.jp/soshiki/030/1708/g/gaiyo/21046.html [8] TAKUYA M，ISMAIL A，YUTARO A，et al. Developing dense wireless signal and magnetic field mapping tool［C］// 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events（PerCom Workshops），Biarritz，France，2024:370-372. [9] BAILEY T，DURRANT-WHYTE H. Simultaneous localization and mapping（SLAM）:Part II［J］. IEEE Robotics & Automation Magazine，2006（13）:108-117. [10] LABBÉ M，MICHAUD F. RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation［J］. J Field Robot，2018（36）:416-446. [11] VINCENT P，LAROCHELLE H，LAJOIE I，et al.. Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion［J］. Journal of Machine Learning Research，2010，11（12）:3371– 3408. [12] YOUSSEF M，AGRAWALA A. Small-scale compensation for WLAN location determination systems［C］// Proc IEEE WCNC，2003:1974-1978. [13] ABBAS M，ELHAMSHARY M，RIZK H，et al. WiDeep:WiFi-based accurate and robust indoor localization system using deep learning［C］// IEEE PerCom，2019. -
点击查看大图
计量
- 文章访问数: 13
- HTML全文浏览量: 3
- PDF下载量: 0
- 被引次数: 0
下载:
