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<p>In recent years, WiFi has garnered increasing attention due to its fine-grained Channel State Information (CSI). Theoretically, more information from the transmitter implies greater potential for accurate localization. To enhance indoor positioning accuracy, this paper proposes an EfficientNetV2-based calibration localization system that integrates Physical Layer (PHY) information from CSI and Medium Access Control Layer (MAC) information from Received Signal Strength Indicator (RSSI), referred to as the Effrf system. The system first eliminates outliers using the Robust Random Cut Forest (RRCF) algorithm and then constructs an extended grid fingerprint database through radial basis function interpolation. In the offline phase, three-dimensional feature map fingerprints based on CSI amplitude, CSI phase, and RSSI are constructed, referred to as CSR feature map fingerprints. The EfficientNetV2 model is then trained to establish the correspondence between the fingerprints and their corresponding positions. During the online phase, to reduce data preprocessing time and ensure real-time positioning, the RRCF streaming anomaly detection method is employed to eliminate outliers. The EfficientNetV2 model, accelerated by GPU, is then used for position estimation. To further enhance accuracy, a random forest (RF) model is utilized to correct high-error localization results generated by the EfficientNetV2 model. Under indoor Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, the proposed system achieves average localization errors of 0.5962&#xa0;m and 1.0306&#xa0;m, respectively.</p>

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The EfficientNetV2-based correction localization combining CSI and RSSI

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Chundong Lu,
  • Junna Shang,
  • Wei Liao,
  • Mingkun Su,
  • Yanzhao Ding

摘要

In recent years, WiFi has garnered increasing attention due to its fine-grained Channel State Information (CSI). Theoretically, more information from the transmitter implies greater potential for accurate localization. To enhance indoor positioning accuracy, this paper proposes an EfficientNetV2-based calibration localization system that integrates Physical Layer (PHY) information from CSI and Medium Access Control Layer (MAC) information from Received Signal Strength Indicator (RSSI), referred to as the Effrf system. The system first eliminates outliers using the Robust Random Cut Forest (RRCF) algorithm and then constructs an extended grid fingerprint database through radial basis function interpolation. In the offline phase, three-dimensional feature map fingerprints based on CSI amplitude, CSI phase, and RSSI are constructed, referred to as CSR feature map fingerprints. The EfficientNetV2 model is then trained to establish the correspondence between the fingerprints and their corresponding positions. During the online phase, to reduce data preprocessing time and ensure real-time positioning, the RRCF streaming anomaly detection method is employed to eliminate outliers. The EfficientNetV2 model, accelerated by GPU, is then used for position estimation. To further enhance accuracy, a random forest (RF) model is utilized to correct high-error localization results generated by the EfficientNetV2 model. Under indoor Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, the proposed system achieves average localization errors of 0.5962 m and 1.0306 m, respectively.