Automotive Radar Interference Mitigation using a Convolutional Autoencoder

Fuchs, Jonas; Dubey, Anand Kumar; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian

doi:10.1109/radar42522.2020.9114641

Cited by 65 publications

(52 citation statements)

References 7 publications

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“…Moving beyond single-radar scenarios, different authors started exploring the use of machine learning algorithms to detect and mitigate interference between various radar types. The scope of work ranges from classifying the interference type [132] to interference mitigation using denoising neural networks such as convolutional neural network [133], autoencoder [134], [135], or recurrent neural networks [136].…”

Section: E Machine Learning and Automotive Radarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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Section: E Machine Learning and Automotive Radarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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“…The authors reported promising results, but they still have concerns regarding the generalization capacity on real data. Another approach that relies on CNNs is proposed in [16]. The authors employ an autoencoder based on the U-Net architecture [17], which performs interference mitigation as a denoising task directly on the range-Doppler spectrum.…”

Section: Related Workmentioning

confidence: 99%

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

Ristea¹,

Anghel²,

Ionescu³

2020

Preprint

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The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

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“…Various signal processing techniques have been proposed to address the problem of mutual interference [6][7][8][9][10][11][12][13][14] in the frequency-modulated continuous wave (FMCW) radar or the chirp sequence (CS) radar [15]. Besides the traditional signal processing approaches, recent research results have shown that deep learning approaches can be used to solve this problem [16][17][18]. A recurrent neural network (RNN) model with self attention mechanism is trained in [16] and applied to suppress the interference-contaminated signal in the time domain.…”

Section: Introductionmentioning

confidence: 99%

“…A convolutional autoencoder is introduced in [18] for noise suppression of the RD spectrum. The prominent target peaks can be preserved after applying the autoencoder used in [18], whereby the phase information and probably the weak target peaks cannot be completely retained.…”

Section: Introductionmentioning

confidence: 99%

“…For the performance assessment, the trained autoencoder has been applied to real radar measurements. Different from [18], the proposed autoencoder reconstructs interference-contaminated samples in the time domain without suffering from difficulties in the reconstruction of phase. Furthermore, the proposed autoencoder can completely avoid the costly and challenging labeling of the radar sensor data as in [16,17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A DNN Autoencoder for Automotive Radar Interference Mitigation

Chen

Taghia

Fei

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, a novel interference mitigation approach using an autoencoder in combination with a traditional interference detection filter is introduced. It is shown that by employing the gated convolution, the encoder has the ability to learn the signal pattern from the remaining interference-free signal. The decoder can recover the interference-contaminated signal segments from the bottleneck representation as computed by the encoder.Experimental results show that the proposed method can provide a remarkable improvement in signal-to-interference-plus-noise ratio (SINR) and preserves its robustness on real radar measurements in severely disturbed scenarios that are more complex than the training dataset.

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Automotive Radar Interference Mitigation using a Convolutional Autoencoder

Cited by 65 publications

References 7 publications

Automotive Radar — From First Efforts to Future Systems

Automotive Radar — From First Efforts to Future Systems

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

A DNN Autoencoder for Automotive Radar Interference Mitigation

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