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

16 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
Number of pages6
StatePublished - 22 Sep 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)
Country/TerritoryUnited States


  • constant false alarm rate (CFAR)
  • target detection
  • deep learning
  • neural network


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