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

Abstract

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).

Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

“…Secondly, we propose a 1D CNN model, which consists of two cases. The CNN model is known to be a trainable model with spatial information of the image retained [21,22]. Because CNNs exhibit excellent performance by extracting features from raw data during image classification, a 1D CNN was recently developed to reduce the computational complexity of 1D signals [23].…”

“…The hyperparameters of the CNN model are the same as those of the proposed DNN model, as summarized in Table 1. [21,22]. Because CNNs exhibit excellent performance by extracting features from raw data during image classification, a 1D CNN was recently developed to reduce the computational complexity of 1D signals [23].…”

Identification of Human Motion Using Radar Sensor in an Indoor Environment

“…Recently, many studies have been conducted to classify targets by training radar data with the CNN. For example, CNN models to classify hand gestures were designed [ 16 , 17 ], and CNN-based classifiers for classifying radar waveforms were also proposed [ 18 , 19 ]. In our study, different CNN structures are determined according to the number of classes to be distinguished, and then classification performance is evaluated for each structure.…”

Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System

“…That is, only by knowing the type of interference that the wireless system is suffering, then it is possible to develop effective method for interference suppression or elimination. Till now, though lots of work have been done for the interference identification problem and many algorithms have been proposed for various wireless systems [2]- [20]. However, currently, the emergence of many new interference waveforms seriously degrade the performance of these algorithms [15]- [20].…”

“…Generally speaking, from the perspective of the application field, the research on interference identification mainly focuses on the radar system [2] and the wireless based communication system [3]- [20]. Since our focus is the later, thus the research status of this filed is summarized in the following.…”