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This work investigates the issue of using channel state information (CSI) collected from a single link to achieve passive indoor personnel positioning in intricate indoor environments. Frequency features and temporal features are extracted separately for the input data using a hybrid model of a deep residual network (ResNet) and temporal convolutional network (TCN). First, we adaptively filter the amplitude information to remove outliers and perform phase calibration on the phase information to recover the true phase. Then, we employ a deep ResNet to extract frequency features in CSI data and enhance the focus on global features by adding a convolutional block attention module (CBAM). Furthermore, we utilize a TCN to extract temporal features in CSI data, to capture local and long-term dependencies efficiently. Finally, we experimentally verify the feasibility of this method using a CSI acquisition platform built with TP-LINK routers and desktop computers equipped with Intel 5300 network cards.

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Passive Indoor Localization Based on a Hybrid Model Using Deep Residual and Temporal Convolutional Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Yunling Chen,
  • Aohao Wang,
  • Yujia Han,
  • Tuanwei Tian,
  • Hao Deng,
  • Dongming Zhang,
  • Rui Ma

摘要

This work investigates the issue of using channel state information (CSI) collected from a single link to achieve passive indoor personnel positioning in intricate indoor environments. Frequency features and temporal features are extracted separately for the input data using a hybrid model of a deep residual network (ResNet) and temporal convolutional network (TCN). First, we adaptively filter the amplitude information to remove outliers and perform phase calibration on the phase information to recover the true phase. Then, we employ a deep ResNet to extract frequency features in CSI data and enhance the focus on global features by adding a convolutional block attention module (CBAM). Furthermore, we utilize a TCN to extract temporal features in CSI data, to capture local and long-term dependencies efficiently. Finally, we experimentally verify the feasibility of this method using a CSI acquisition platform built with TP-LINK routers and desktop computers equipped with Intel 5300 network cards.