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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-1220-6
ISBN (Print)978-1-7281-1221-3
DOIs
Publication statusPublished - 1 Sep 2019
Publication typeA4 Article in a conference publication
EventIEEE Vehicular Technology Conference -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465

Conference

ConferenceIEEE Vehicular Technology Conference
Period1/01/00 → …

Abstract

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.

Keywords

  • Signal to noise ratio, Noise level, Training, Biological neural networks, Deep learning, Estimation

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