Modulation type classification of interference signals in automotive radar systems

Kim, Jin-Wook; Lee, Seongwook; Kim, Seong-Cheol

doi:10.1049/iet-rsn.2018.5521

Cited by 12 publications

(12 citation statements)

References 22 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We set five different modulations for the interference signal: the unmodulated CW, slow chirp FMCW, fast chirp FMCW, pulsed CW, and FSK signals. In [9], the mathematical expressions of the interference signals are presented when a fast chirp FMCW radar is used for target detection. The expressions are shown in Table 1.…”

Section: B Range-doppler Response Of the Interference Signalmentioning

confidence: 99%

“…In simulation results, the proposed CNN-based method can classify five different interference signals with an accuracy of over 96% and it shows better classification accuracy than SVM's used in [8], [9]. Unlike feature-based machine learning techniques, our proposed method does not require handcrafted features based on domain knowledge.…”

Section: Introductionmentioning

confidence: 98%

“…Therefore, we need an effective method to identify the type of interference. Several studies focusing on interference recognition have been conducted [7]- [9]. In [7], the authors proposed a method for classifying interference signals using Gini's coefficient.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it seems that this classification process requires considerable computation. The authors in [9] extracted features based on the statistical characteristics of the radar signal and used SVM to determine the modulation type of signal that interfered with the received signal. However, this model required full chirp data for feature extraction resulting in a time delay in the classification.…”

Section: Introductionmentioning

confidence: 99%

“…We verified that the accuracy increased as the number of chirps used increased. We also compared the classification performance with the methods proposed in [8], [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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. INDEX TERMS Automotive radar, modulation type classification, mutual interference, convolutional neural network (CNN).

show abstract

Section: B Range-doppler Response Of the Interference Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…We verified that the accuracy increased as the number of chirps used increased. We also compared the classification performance with the methods proposed in [8], [9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations