An Indoor Localization System Using Residual Learning with Channel State Information

Xu, Chendong; Wang, Weigang; Zhang, Yunwei; Qin, Jie; Yu, Shujuan

doi:10.3390/e23050574

Cited by 4 publications

(2 citation statements)

References 26 publications

(26 reference statements)

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“…However, recent research on activity tracking using radio frequency signals from wireless networks provides an attractive solution for device-free awareness. References [1][2][3] use Wi-Fi to achieve personnel positioning without equipment.…”

Section: Introductionmentioning

confidence: 99%

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion

Niu,

He,

Fang

et al. 2024

Sensors

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The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.

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Section: Introductionmentioning

confidence: 99%

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion

Niu,

He,

Fang

et al. 2024

Sensors

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“…When the working mode of the wireless device is high-throughput [11], we can easily obtain CSI data by the IEEE 802.11 n standard. To address the aforementioned advantages, researchers have explored several indoor localization methods by utilizing CSI to construct images [12,13]. However, these methods have some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Intelligent indoor localization based on CSI via radio images and deep learning

Zhang

Fan

2023

Meas. Sci. Technol.

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The intelligent indoor localization based on WIFI is increasingly concerned for its universality. However, in practical applications, its indoor localization accuracy is limited by noises, diffractions and multipath effects. To overcome these drawbacks, we design a new intelligence indoor localization system based on Channel State Information (CSI) of the wireless signal from Multiple Input Multiple Output (MIMO), named IILC. In IILC, the initial amplitude information is first processed in the measured CSI data, which can effectively suppress the impact from noise and other interference. Next, we explore a method to construct radio image. It can make full use of space-frequency information and time-frequency information from CSI-MIMO to obtain more location information. Then, we design a new deep learning network to obtain the optimal effective of radio image classification. Moreover, a mixed-norm is proposed to impose sparsity penalty and overfit constraint on the loss function, which makes the valuable feature units active and the others inactive. The experimental results verify that IILC system has excellent performance. The overall localization accuracy of IILC in the office scene can reach 97.10%, and the probability of localization error within 1.2m is 86.21%.

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