Deep Learning Model for Robust Target Tracking Using TDoA Probabilistic Image 


Vol. 48,  No. 7, pp. 807-815, Jul.  2023
10.7840/kics.2023.48.7.807


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  Abstract

Ultra Wide Band (UWB) based indoor positioning methods utilizing Time Difference of Arrival (TDoA) are commonly used; however, their performance significantly degrades in environments with high Additive White Gaussian Noise (AWGN). Although many studies have attempted to remove AWGN from TDoA, these approaches require additional positioning methods and lack robustness in various environments. In this paper, we propose a 'TDoA Probabilistic Image Based Target Tracking (TPITT)' method that robustly estimates an object's position using TDoA with AWGN. TPITT generates probability images of object presence in each region using TDoA and employs a 'Convolution-LSTM' model to estimate object coordinates. Experiments demonstrate the proposed method's robustness and low prediction error across diverse environments. Notably, TPITT is more effective than the prior study 'TDoA Image Based Target Tracking (TITT)' in environments with high AWGN.

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[IEEE Style]

S. Lee and J. Shim, "Deep Learning Model for Robust Target Tracking Using TDoA Probabilistic Image," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 7, pp. 807-815, 2023. DOI: 10.7840/kics.2023.48.7.807.

[ACM Style]

Sungho Lee and Jaewoong Shim. 2023. Deep Learning Model for Robust Target Tracking Using TDoA Probabilistic Image. The Journal of Korean Institute of Communications and Information Sciences, 48, 7, (2023), 807-815. DOI: 10.7840/kics.2023.48.7.807.

[KICS Style]

Sungho Lee and Jaewoong Shim, "Deep Learning Model for Robust Target Tracking Using TDoA Probabilistic Image," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 7, pp. 807-815, 7. 2023. (https://doi.org/10.7840/kics.2023.48.7.807)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880