In recent years, there have been abundant researches focused on indoor human presence detection based on laborious supervised learning (SL) and channel state information (CSI). These existing studies adopt spatial information of CSI to improve detection accuracy. However, channel is susceptible to arbitrary environmental changes in practice, such as the object movement, atmospheric factors and machine rebooting, which leads to degraded prediction accuracy. However, the existing SL-based methods require to re-train a new model with time-consuming labeling. Therefore, designing a semi-supervised learning (SSL) based scheme by continuously monitoring model "life-cycle" becomes compellingly imperative. In this paper, we propose bifold teacher-student (BTS) learning for presence detection system, which combines SSL by utilizing partial labeled and unlabeled dataset. The proposed primal-dual teacher-student network is capable of intelligently learning spatial and temporal features from labeled and unlabeled CSI. Additionally, the enhanced penalized loss function leveraging entropy and distance measure can distinguish the drifted data, i.e., features of new dataset are affected by time-varying effect and are alternated from the original distribution. The experimental results demonstrate that the proposed BTS system can sustain the asymptotic accuracy after retraining the model with unlabeled data. Moreover, label-free BTS outperforms the existing SSL-based models in terms of the highest detection accuracy, while achieving the similar performance of SL-based methods.
翻译:近些年来,已经开展了大量研究,重点是在艰苦、有监督的学习(SL)和传送国家信息(CSI)基础上的室内人的存在检测。这些现有研究采用CSI的空间信息来提高检测准确性。然而,频道容易在实践上任意的环境变化,如物体移动、大气因素和机器重新启动,导致预测准确性下降。然而,基于SL的现有方法需要重新培训一个带有耗时标签的新模型。因此,通过持续监测模型“生命周期”来设计一个半监督的学习(SSL)计划变得势在必行。在本文件中,我们建议将访问检测系统的师生双倍学习结合起来,通过使用部分贴标签和未贴标签的数据集进行整合。拟议的原始教师-学生网络能够从标签和未贴标签的CSI中明智地学习空间和时间特征。此外,利用基于英特罗比和远程测量的强化惩罚性损失功能可以区分漂浮数据,即,新数据设置的教师-学生学习者(BTS)学习双倍的学习系统,这通过部分贴标签进行部分的校准的校准性再分析结果,可以维持现有的SL格式,同时显示原的测试结果。