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 language||American English|
|State||Published - 2019|
|Event||2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) - Honolulu, United States|
Duration: 22 Sep 2019 → 25 Sep 2019
|Conference||2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)|
|Period||22/09/19 → 25/09/19|