Multiple Target Detection For OFDM Radar Based On Convolutional Neural Network

• Published in: Turkish journal of computer and mathematics education 2021-04, Vol.12 (6), p.544-550
• Main Authors: Choi, Jae-Woong, Jeong, Eui-Rim
• Format: Article
• Language: English
• Subjects: 6G mobile communication; Algorithms; Artificial neural networks; Computer simulation; Fast Fourier transformations; Fourier transforms; Moving targets; Neural networks; Orthogonal Frequency Division Multiplexing; Radar detection; Radar equipment; Search algorithms; Target detection; Target recognition
• ISSN: 1309-4653; 1309-4653
• DOI: 10.17762/turcomat.v12i6.1976

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.