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Device-free indoor localization based on sparse coding with nonconvex regularization and adaptive relaxation localization criteria

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Abstract

Considering practical device-free localization (DFL), the localization precision is usually proportional to the sensor density, with an ordinary arrangement. A better localization performance generally indicates a higher density of the sensor nodes that need to be deployed. To overcome this problem, we propose a new framework for dispatching sensor nodes. Regarding this framework, only few sensor nodes are applied to achieve an excellent localization performance. We consider the DFL problem as a sparse coding problem. To maintain the convexity of the cost function for more accurate solutions, we introduce the generalized minimax-concave (GMC) regularization to approximate the \(\ell _0\)-norm regularization. The global optimal solution can be identified by adopting the forward-backward splitting algorithm (FBS). Furthermore, the localization error is further decreased by the proposed adaptive relaxation localization (ARL) criteria for target localization. We tackled two experimental scenes in a real laboratory and compared the performance of the proposed algorithm with that of other algorithms using different regularizations. The experimental results show 100\(\%\) grid localization accuracy under the \(0.5 \times 0.5\) m grid scene. After adopting the ARL criteria, the average localization error decreased from 0.098 to 0.053 m in the \(0.25\times 0.25\)  m grid scene, with an increased rate of 45.9\(\%\). This is the best performance compared to state-of-the-art framework.

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Acknowledgements

This study was supported in part by the Guangxi Postdoctoral Special Foundation and the National Natural Science Foundation of China under Grant 61903090 and 62076077, the Dean Project of Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory.

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Authors and Affiliations

  1. Guilin University of Electronic Technology, Guilin, China

    Kangkang Zhang, Benying Tan, Shuxue Ding, Yujie Li & Guangwei Li

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  1. Kangkang Zhang
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  2. Benying Tan
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  3. Shuxue Ding
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  4. Yujie Li
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  5. Guangwei Li
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Correspondence to Benying Tan or Shuxue Ding.

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Zhang, K., Tan, B., Ding, S. et al. Device-free indoor localization based on sparse coding with nonconvex regularization and adaptive relaxation localization criteria. Int. J. Mach. Learn. & Cyber. 14, 429–443 (2023). https://doi.org/10.1007/s13042-022-01559-x

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