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Device-Free Indoor Multi-target Tracking in Mobile Environment

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Abstract

Indoor multiple target tracking is a promising research field that attracts many efforts. Traditional approaches for tackling this problem are usually model-based methods. WiFi-based tracking approaches suffer from high cost in retrieving the CSI information. Most RF signal-based methods provide a mathematical framework correlating movement in space to a link’s RSS value. Real RSS values are used to model the signal attenuation, and the distance correlation with signal attenuation is used to estimate locations. In this paper, we propose DCT, a noise-tolerant, unobtrusive and device-free tracking framework. DCT adopts density-based clustering to find the centers. We further use a linear function of mean RSS variances and target amount and FCM algorithm to adjust the number of targets and positions. The multiple particle filter (MPF) is adopted to refine the target tracking accuracy. DCT is tolerant for noise and multi-path effects, and can fast simultaneously tracking with a O(N) time complexity. The extensive experiments in trace-driven simulations and real implementations show that DCT is efficient and effective in tracking multiple target, and can achieve a high precision.

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Acknowledgment

This paper is supported by National Natural Science Foundation of China (No. 61502374, 61802291, 61702391 and 61902236), National Key Research and Development Project (No. 2019YFB1406400), China Postdoctoral Science Foundation (No. BX20180235), and the Fundamental Research Funds for Central Universities (No. JB190304).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, China

    Rui Li, Zhiping Jiang, Yueshen Xu, Fushan Chen & Junzhao Du

  2. Computing Center, Shanghai University, Shanghai, China

    Honghao Gao

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  1. Rui Li
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  2. Zhiping Jiang
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  3. Yueshen Xu
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  4. Honghao Gao
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  5. Fushan Chen
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  6. Junzhao Du
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Correspondence to Yueshen Xu.

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Li, R., Jiang, Z., Xu, Y. et al. Device-Free Indoor Multi-target Tracking in Mobile Environment. Mobile Netw Appl 25, 1195–1207 (2020). https://doi.org/10.1007/s11036-020-01540-4

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