Classification of Interference Signal for Automotive Radar Systems With Convolutional Neural Network

When a radar signal generated by another vehicle arrives at an ego-vehicle, mutual interference occurs, which can seriously degrade the detection performance of the radar. To mitigate mutual interference, the type of radar modulation used in the interference vehicle must be identified because the ty...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.176717-176727
Main Authors: Kim, Jinwook, Lee, Seongwook, Kim, Yong-Hwa, Kim, Seong-Cheol
Format: Article
Language:English
Subjects:
Artificial neural networks
Automotive engineering
Automotive radar
Chirp
Continuous radiation
convolutional neural network (CNN)
Fourier transforms
Interference
Keying
Machine learning
Military communications
Modulation
modulation type classification
mutual interference
Neural networks
Radar detection
Radar equipment
Radar systems
Signal classification
Support vector machines
Two dimensional displays
Waveforms
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Summary:When a radar signal generated by another vehicle arrives at an ego-vehicle, mutual interference occurs, which can seriously degrade the detection performance of the radar. To mitigate mutual interference, the type of radar modulation used in the interference vehicle must be identified because the types of radar systems installed in each vehicle are different. Therefore, in this paper, we propose a method for classifying the modulation types of interference signals in automotive fast chirp frequency modulated continuous waveform (FMCW) radar systems. We build a mathematical model of the received signal when the radar signal transmitted by the ego-vehicle interferes with various types of interference signals, such as unmodulated continuous wave (CW), slow chirp FMCW, fast chirp FMCW, pulsed CW, and frequency-shift keying signals. In the fast chirp FMCW radar systems, the received signal is converted into range-Doppler response using two-dimensional Fourier transform. Based on range-Doppler responses of the interference signals, we design a classifier to identify the modulation type of interference signals using a convolutional neural network (CNN). Through our proposed CNN, we can classify five different types of interference signals with an accuracy of over 96%. In addition, compared to conventional feature-based machine learning techniques such as support vector machines, the proposed method can effectively identify the interference signal with fewer input signals in shorter time.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3026749