| Paper ID | MLSP-47.2 |
| Paper Title |
A DNN AUTOENCODER FOR AUTOMOTIVE RADAR INTERFERENCE MITIGATION |
| Authors |
Shengyi Chen, Ruhr-Universität Bochum & HELLA GmbH & Co. KGaA, Germany; Jalal Taghia, Tai Fei, Uwe Kühnau, HELLA GmbH & Co. KGaA, Germany; Nils Pohl, Rainer Martin, Ruhr-Universität Bochum, Germany |
| Session | MLSP-47: Applications of Machine Learning |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
