Radar Signal Detection Applying Deep Learning to the Correlation Between Adjacent Windows in Time Domain 


Vol. 46,  No. 7, pp. 1153-1155, Jul.  2021
10.7840/kics.2021.46.7.1153


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  Abstract

For a pulse radar signal, the radar pulses tend to occur periodically in time domain. Based on this, a previous work presented a radar detection scheme which performs circular convolution between two signal sets in two adjacent observation windows in time domain and employs its maximum absolute value as a test statistic. In this Letter, in order to further improve its performance, we present two improved versions of it which apply deep learning to the circular convolution between signals over two adjacent observation windows and the absolute values of Fourier transforms of the convolution and compares their performances.

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  Cite this article

[IEEE Style]

C. H. Lim and J. Park, "Radar Signal Detection Applying Deep Learning to the Correlation Between Adjacent Windows in Time Domain," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 7, pp. 1153-1155, 2021. DOI: 10.7840/kics.2021.46.7.1153.

[ACM Style]

Chang Heon Lim and Jongbu Park. 2021. Radar Signal Detection Applying Deep Learning to the Correlation Between Adjacent Windows in Time Domain. The Journal of Korean Institute of Communications and Information Sciences, 46, 7, (2021), 1153-1155. DOI: 10.7840/kics.2021.46.7.1153.

[KICS Style]

Chang Heon Lim and Jongbu Park, "Radar Signal Detection Applying Deep Learning to the Correlation Between Adjacent Windows in Time Domain," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 7, pp. 1153-1155, 7. 2021. (https://doi.org/10.7840/kics.2021.46.7.1153)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880