Automotive Radar Interference Mitigation using a Convolutional Autoencoder

Fuchs J, Dubey A, Lübke M, Weigel R, Lurz F (2020)

Publication Language: English

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Future Publication Type: Conference contribution

Publication year: 2020

Event location: Washington DC

DOI: 10.1109/RADAR42522.2020.9114641

Abstract

Automotive radar interference imposes big challenges on signal processing algorithms as it raises the noise floor and consequently lowers the detection probability. With limited frequency bands and increasing number of sensors per car, avoidance techniques such as frequency hopping or beamforming quickly become insufficient. Detect-and-repair strategies have been studied intensively for the automotive field, to reconstruct the affected signal samples. However depending on the type of interference, reconstruction of the time domain signals is a highly non-trivial task, which can affect following signal processing modules. In this work an autoencoder based convolutional neural network is proposed to perform image based denoising. Interference mitigation is phrased as a denoising task directly on the range-Doppler spectrum. The neural networks shows significant improvement with respect to signal-to-noise-plus-interference ratio in comparison to other state-of-the-art mitigation techniques, while better preserving phase information of the spectrum compared to other techniques.

Authors with CRIS profile

Related research project(s)

How to cite

APA:

Fuchs, J., Dubey, A., Lübke, M., Weigel, R., & Lurz, F. (2020). Automotive Radar Interference Mitigation using a Convolutional Autoencoder. In Proceedings of the 2020 International Conference on Radar (RADAR). Washington DC, US.

MLA:

Fuchs, Jonas, et al. "Automotive Radar Interference Mitigation using a Convolutional Autoencoder." Proceedings of the 2020 International Conference on Radar (RADAR), Washington DC 2020.

BibTeX: Download