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Indoor device-free passive localization with DCNN for location-based services

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Abstract

With the increasing demand of indoor location-based services, such as tracking targets in a smart building, device-free localization technique has attracted great attentions because it can locate the targets without employing any attached devices. Due to the limited space and complexity of the indoor environment, there still exist challenges in terms of high localization accuracy and high efficiency for indoor localization. In this paper, for addressing such issues, we first convert the received signal strength (RSS) signals into image pixels. The localization problem is then formulated as an image classification problem. To well handle the variant RSS images, a deep convolutional neural network is then structured for classification. Finally, for validating the proposed scheme, two real testbeds are built in the indoor environments, including a living room and a corridor of an apartment. Experimental results show that the proposed scheme achieves good localization performance. For example, the localization accuracy can reach up to 100% in the scenario of living room and 97.6% in the corridor. Moreover, the proposed approach outperforms the methods of the K-nearest-neighbor and the support vector machines in both the noiseless and noisy environments.

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Acknowledgements

This work was partially supported by the JSPS Kiban(B) (Project Number 18H03240) and the JSPS Kiban(C) (Project Number 18K11298), Japan, and in part by the National Natural Science Foundation of China (Grant Number: 61822202 and 61872089).

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Lingjun Zhao, Chunhua Su, Zeyang Dai, Huakun Huang & Zhaoyang Han

  2. School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin-City, Guangxi, China

    Shuxue Ding

  3. College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China

    Shuxue Ding

  4. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China

    Xinyi Huang

  5. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, 518055, China

    Chunhua Su

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  1. Lingjun Zhao
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  2. Chunhua Su
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  3. Zeyang Dai
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  4. Huakun Huang
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  5. Shuxue Ding
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  7. Zhaoyang Han
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Correspondence to Lingjun Zhao, Chunhua Su or Shuxue Ding.

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Zhao, L., Su, C., Dai, Z. et al. Indoor device-free passive localization with DCNN for location-based services. J Supercomput 76, 8432–8449 (2020). https://doi.org/10.1007/s11227-019-03110-2

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