Underwater Target Tracking Using Data Association Based on Neural Networks 


Vol. 43,  No. 12, pp. 2006-2013, Dec.  2018
10.7840/kics.2018.43.12.2006


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  Abstract

Nearest neighbor Kalman filter (NNKF) and probabilistic data association (PDAF) target tracking techniques are often used to track targets in under water. However, these algorithms have a problem of finding the optimum parameters for the Kalman filter according to the underwater environment. In this paper, we propose a target tracking algorithm based on data association technique using artificial neural network to solve this problem. Through computer simulations, we compared the target tracking performance of the proposed algorithm with the NNKF and PDAF algorithms.

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  Cite this article

[IEEE Style]

D. Cha, J. Kim, D. S. Han, "Underwater Target Tracking Using Data Association Based on Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 12, pp. 2006-2013, 2018. DOI: 10.7840/kics.2018.43.12.2006.

[ACM Style]

Daewoong Cha, Juho Kim, and Dong Seog Han. 2018. Underwater Target Tracking Using Data Association Based on Neural Networks. The Journal of Korean Institute of Communications and Information Sciences, 43, 12, (2018), 2006-2013. DOI: 10.7840/kics.2018.43.12.2006.

[KICS Style]

Daewoong Cha, Juho Kim, Dong Seog Han, "Underwater Target Tracking Using Data Association Based on Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 12, pp. 2006-2013, 12. 2018. (https://doi.org/10.7840/kics.2018.43.12.2006)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880