Performance Analysis of Sequence-based Deep Learning Model for LPI Radar Waveform Recognition in Fading Channel

이동은; 김윤지; Yoon, Dongweon

doi:10.1109/ICTC55196.2022.9953019

상세 보기

Performance Analysis of Sequence-based Deep Learning Model for LPI Radar Waveform Recognition in Fading Channel

이동은;
김윤지;
Yoon, Dongweon

Citations

SCOPUS

7

초록

Although many studies have been conducted for the recognition of low probability of intercept (LPI) radar waveforms, only a few consider fading channels because the recognition in the fading channel is more challenging than that in the additive white Gaussian noise channel. In this paper, we investigate the recognition performance of the sequence-based deep learning model for LPI radar waveforms in a fading channel. As inputs of the model, we consider the received radar waveform, its discrete Fourier transform, and its autocorrelation, respectively. Simulation results show that it is advantageous to exploit the discrete Fourier transform of the received radar waveform as the input of the sequence-based recognition model in the fading channel.

키워드

low probability of intercept; radar waveform recognition; sequence-based recognition model; Deep learning; Fading channels; Gaussian noise (electronic); Learning systems; Radar; White noise; Discrete Fourier transforms; Additive white Gaussian noise channel; Fadings channels; Learning models; Low probability of intercept; Performance; Performances analysis; Radar waveform recognition; Radar waveforms; Recognition models; Sequence-based recognition model

제목: Performance Analysis of Sequence-based Deep Learning Model for LPI Radar Waveform Recognition in Fading Channel

저자: 이동은; 김윤지; Yoon, Dongweon

DOI: 10.1109/ICTC55196.2022.9953019

발행일: 2022-10

유형: Conference Paper

저널명: International Conference on ICT Convergence

권: 2022-October

페이지: 2111 ~ 2113