arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2409.00634cs.ITeess.SPmath.IT
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基于测量的室内感知

Indoor Sensing with Measurements

Vijaya Yajnanarayana, Philipp Geuer, Satyam Dwivedi

AI总结:

本文通过室内信道测量研究扰动,提出基于CNN和树集成的人工智能方法,实现单链路超90%准确率的人体检测及三链路下0.7米平均误差的室内定位。

摘要:

蜂窝无线网络正在向获取感知等新能力的方向演进,这将支持新型用例和应用。其中许多应用需要室内感知能力,而这种能力可以通过利用室内信道的扰动来实现。在本工作中,我们开展了一项室内信道测量活动,以研究这些扰动,并开发基于人工智能的算法来估计感知参数。我们基于卷积神经网络(CNN)和基于树的集成架构开发了多种用于感知的人工智能方法。我们表明,利用简单的基于CNN的人工智能算法,可以从单条链路的信道扰动中检测到诸如人体之类的无源目标的存在,准确率超过90%。然而,感知无源目标的位置要困难得多,需要更复杂的人工智能算法和部署方式。我们表明,在具有三条链路的部署条件下,并采用复杂的人工智能架构进行位置估计,室内房间中人的位置可以在平均位置误差0.7米以内被估计出来。我们还将结果与基线算法进行了比较,以证明所提出方法的实用性。

英文摘要:

The cellular wireless networks are evolving towards acquiring newer capabilities, such as sensing, which will support novel use cases and applications. Many of these require indoor sensing capabilities, which can be realized by exploiting the perturbation in the indoor channel. In this work, we conduct an indoor channel measurement campaign to study these perturbations and develop AI-based algorithms for estimating sensing parameters. We develop several AI methods based on CNN and tree-based ensemble architectures for sensing. We show that the presence of a passive target like a person can be detected from the channel perturbation of a single link with more than 90 % accuracy with a simple CNN based AI algorithm. However, sensing the position of a passive target is far more challenging requiring more complex AI algorithms and deployments. We show that the position of the human in the indoor room can be estimated within the average position error of 0.7 m with a deployment having three links and employing complex AI architecture for position estimation. We also compare the results with the baseline algorithm to demonstrate the utility of the proposed method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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