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WiFi-Sensing Based Person-to-Person Distance Estimation Using Deep Learning

机译：基于WiFi感测的人对人与人距离估计使用深度学习

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摘要

Accurately estimating the distance between persons with COTS mobile devices can benefit many applications (e.g., group activity analysis, indoor navigation, etc.). In this paper we present WiDE, a deep learning-based system for estimating person-to-person distance based on surrounding WiFi signals. Specifically, WiDE has two phases: offline learning and online prediction. In offline learning phase, we apply a stacked autoencoder (SAE) for pre-training the weights of a deep neural network (DNN), and establish a DNN-based classifier for predicting between-person discretized distance and corridor identity using WiFi signals. During online prediction phase, based on the trained DNN with the SAE and newly uploaded WiFi information, we estimate the corridor identities and the distance between pairwise persons. We validate our system by conducting extensive experiments in a three-floor campus building, and the results show that WiDE achieves the corridor identification accuracy over 98 % and the median ranging error of 0.9m and 3.0m for two persons on the same corridor and on different corridors, respectively, which outperforms the state-of-the-art proximity inferring system [1].
机译：准确地估计COTS移动设备的人与人之间的距离可以使许多应用（例如，组活动分析，室内导航等）受益。在本文中，我们呈广泛，基于深度学习的系统，用于估计基于周围的WiFi信号的人到人距离。具体而言，广泛有两个阶段：离线学习和在线预测。在离线学习阶段，我们应用一个堆叠的AutoEncoder（SAE）以预先训练深神经网络（DNN）的权重，并建立基于DNN的分类器，用于使用WiFi信号预测人之间的离散距离和走廊标识。在在线预测阶段，基于具有SAE和新上传的WiFi信息的训练的DNN，我们估计走廊标识和成对人员之间的距离。我们通过在三楼的校园大楼进行广泛的实验来验证我们的系统，结果表明，同一走廊上的两个人和两个人的中位数测距误差和3.0米的中位数测距误差有超过98℃，结果在不同的走廊上，分别优于最先进的近距离推断系统[1]。

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