DL-CFAR: a Novel CFAR Target Detection Method Based on Deep Learning

Chia Hung Lin, Yu Chien Lin, Yue Bai, Wei-Ho Chung, Ta-Sung Lee, Heikki Huttunen

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


The well-known cell-averaging constant false alarm rate (CA-CFAR) scheme and its variants suffer from masking effect in multi-target scenarios. Although order-statistic CFAR (OS-CFAR) scheme performs well in such scenarios, it is compromised with high computational complexity. To handle masking effects with a lower computational cost, in this paper, we propose a deep-learning based CFAR (DL- CFAR) scheme. DL-CFAR is the first attempt to improve the noise estimation process in CFAR based on deep learning. Simulation results demonstrate that DL-CFAR outperforms conventional CFAR schemes in the presence of masking effects. Furthermore, it can outperform conventional CFAR schemes significantly under various signal-to-noise ratio conditions. We hope that this work will encourage other researchers to introduce advanced machine learning technique into the field of target detection.
Original languageAmerican English
StatePublished - 2019
Event2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) - Honolulu, United States
Duration: 22 Sep 201925 Sep 2019


Conference2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
CountryUnited States

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