콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Multiple target detection for OFDM radar based on convolutional neural network
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2021-04
- 저자명 정의림공동(교신),최재웅
- 학술지명 Turkish Journal of Computer and Mathematics Education
- 발행처명 TURCOMAT
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 2
논문 초록 (Abstract)
The objective of this paper is to propose a multiple target identification technique for orthogonal frequency division multiplexing (OFDM) radars. First, a 2-D (range & Doppler) periodogram is obtained from the reflected signal through 2-D fast Fourier transform (FFT) of the received OFDM symbols. Usually, the peaks of the periodogram indicates the targets. Conventionally, peak search algorithms are used to find the multiple targets. In this paper, however, a convolutional neural network (CNN) classifier is proposed to identify the targets. The proposed technique does not need any additional information but the 2-D periodogram while the conventional method requires the noise variance as well as the periodogram. The performance is examined through computer simulation. According to the results, if the number of maximum identifiable targets are small, the proposed technique performs well. However, as the number increases, the detection accuracy decreases. In the simulation environments, the proposed method outperforms the conventional one. The proposed OFDM radar technique can be applied to 6G mobile communications to identify the moving targets around the transmitter without additional frequency resource for radar systems.
