| Autor: |
윤종현, 이재승, 주종한, 정태환, 박정용, 이동휘 |
| Předmět: |
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| Zdroj: |
Journal of Korean Institute of Electromagnetic Engineering & Science / Han-Guk Jeonjapa Hakoe Nonmunji; Dec2023, Vol. 34 Issue 12, p927-935, 9p |
| Abstrakt: |
When a high-power jamming signal is received on a radar, the signal to interference plus noise ratio (SINR) decreases, and the detection/tracking performance of the radar is degraded. One method to restore the SINR of a radar decreased by high-power jamming signals to a jamming signal-free level is adaptive beamforming technology that suppresses jamming signals using signal spatial information. This study proposed an adaptive beamforming weight estimation network for jamming suppression using convolutional neural networks (CNNs) based on unsupervised learning and compared and analyzed the performance with those of linearly constructed minimum variance (LCMV) and conventional sidelobe cancer (CSC), which are existing adaptive beamforming technologies for jamming suppression. The proposed network suppressed the jamming signal by 5.7 dB more than that by CSC but 1.9 dB less than that by LCMV, and the beamforming weight estimation time took approximately 29.41 ms less than that by LCMV but approximately 53.49 ms more than that by CSC. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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