一种基于Wi-Fi的室内人员辨识模型.docx
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2023-02-23
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摘要
准确获取室内不同区域人员数量对于建筑节能、空调与照明系统智能调控以及公共安防等具有重要
意义。为此，提出一种低成本、高辨识准确率的基于 Wi-Fi 的室内人员辨识通用模型。该模型通过
部署多个 Wi-Fi 探针，在离线阶段采集不同环境下的终端样本数据，使用粒子群算法优化 BP 神经
网络，并采用并行方法快速训练人员辨识模型；在在线阶段，基于训练完成的人员辨识模型，根据
实际接收到的 Wi-Fi 信号强度判断人员是否在区域内，最终统计出区域内的人数。在安徽建筑大学
某科研楼搭建了一套基于 Wi-Fi 的室内人员辨识系统，对该模型的有效性进行测试。结果表明，在
实验涉及的所有区域，该人员辨识模型的召回率均大于 90%以上，准确率可达 91%以上，满足建
筑物智能系统快速获取人员数量的实际应用需求。
Abstract
Obtaining the accurate estimation of indoor occupant counting in different areas is
of great significance in many applications， including building energy conservation，
intelligent regulation of air conditioning and lighting systems， and public security.
Therefore，propose a low-cost， high-accuracy Wi-Fi-based indoor personnel
identification general model. This model deploys multiple Wi-Fi probes， collects
terminal sample data in different environments in the offline phase， uses particle
swarm optimization to optimize the BP neural network， and uses a parallel method
to quickly train the personnel identification model； in the online stage， based on
the trained personnel identification model， judge whether the personnel are in the
area according to the actual received Wi-Fi signal strength， and finally count the
number of people in the area. Finally， in a research building of Anhui Jianzhu
University， a Wi-Fi-based indoor people identification system was built， and the
effectiveness of the method was tested through experiments. The results show that
the personnel identification model in all areas involved in the experiment， the
recall rate of the model is greater than 90%， and the model accuracy rate can
reach more than 91%，which meets the practical application requirements of
building intelligent system for the rapid deployment of personnel quantity
measurement method.
译
关键词
Wi-Fi 探针; 人员辨识; BP 神经网络; 粒子群优化; 并行处理
Keywords
Wi-Fi sniffer; personnel identification; BP neural network; particle swarm
optimization; parallel processing
译
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