Multiple Target Detection For OFDM Radar Based On Convolutional Neural Network

Jae-Woong Choi Et.Al

doi:10.17762/turcomat.v12i6.1976

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.

Highlights

Many technologies and applications demand higher communications capacity and bandwidth while the importance of various radio-based sensing schemes is increasing in commercial, industrial and military fields [1]
In [4] and [5], the convergence of mobile communications and radar systems was addressed based on the use of the LTE and 5G NR waveform for radar purposes
The discrimination between vehicle, cyclist and pedestrian by using convolutional neural network (CNN) was proposed for frequency modulated continuous wave (FMCW) radar [6], and a classification technique of real target, clutter, and dense multi-false targets is developed for radar systems based on factorized CNN in [7]

Summary

Introduction

Many technologies and applications demand higher communications capacity and bandwidth while the importance of various radio-based sensing schemes is increasing in commercial, industrial and military fields [1]. The wideband OFDM waveform satisfies the requirements as radar signals for fine resolution of range and Doppler estimation. Due to those reasons, the use of OFDM-based radar systems is receiving growing interest [1,2]. We propose multiple target identification technique for OFDM radar based on CNN. Multiple target detection for OFDM radar based on convolutional neural network considered as direct coupling components and removed. If the OFDM radar system is equipped in a base station, and it collects the periodogram at dawn, only direct coupling components can exist in the periodogram. If two periodograms are used for the CNN input, over 2 dB further improvement can be achieved

System and signal model

Conventional method