Improved Target Detection Through DNN-Based Multi-Channel Interference Mitigation in Automotive Radar-Reference-Cited by-同舟云学术

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Improved Target Detection Through DNN-Based Multi-Channel Interference Mitigation in Automotive Radar

Published:2023 Issue: Volume:1 Page:75-89
ISSN:2832-7357
Container-title:IEEE Transactions on Radar Systems
language:
Short-container-title:Trans. Rad. Sys.

Author:

Chen Shengyi¹^ORCID,Klemp Marvin²,Taghia Jalal³,Kühnau Uwe⁴,Pohl Nils⁵^ORCID,Martin Rainer¹^ORCID

Affiliation:

1. Institute of Communication Acoustics, Ruhr-Universität Bochum, Bochum, Germany

2. Institute of Measurement and Control Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany

3. HELLA Aglaia Mobile Vision GmbH, Berlin, Germany

4. HELLA GmbH & Co. KGaA, Lippstadt, Germany

5. Institute of Integrated Systems, Ruhr-Universität Bochum, Bochum, Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9931145/10012287/10130625.pdf?arnumber=10130625

Reference44 articles.

1. A Two-Stage DNN Model With Mask-Gated Convolution for Automotive Radar Interference Detection and Mitigation

2. Bats-inspired frequency hopping for mitigation of interference between automotive radars

3. Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation

4. Automotive radar interference mitigation using a sparse sampling approach

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Design and performance validation of CWT-MCA based interference mitigation for automotive radars;Digital Signal Processing;2024-10

2. Interference mitigation for FMCW radar via chirp rate estimation and signal separation;Signal Processing;2024-09

3. Interference Mitigation in Automotive Radar using ResNet Deep Neural Network Models;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06

4. Automatic Multipath Annotation for Conventional Automotive Radar Datasets;IEEE Sensors Journal;2024-04-15

5. Efficient Multi-channel Automotive Radar Interference Mitigation Using Pruned and Quantized Neural Networks;2023 IEEE International Radar Conference (RADAR);2023-11-06

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