Deep Convolutional Autoencoder Applied for Noise Reduction in Range-Doppler Maps of FMCW Radars

Marcio Luiz Lima de Oliveira, Marco Jan Gerrit Bekooij

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

17 Citations (Scopus)
305 Downloads (Pure)


In this paper, we discuss the usage of deep Convolutional Autoencoders (CAE) for denoising Range-Doppler (RD) maps of an FMCW radar in a near-field situation with pedestrians and cyclists as moving objects. Traditional methods for noise reduction such as CFAR with Peak Detection (PD) have poor performance under highly noisy environments, especially when objects have low reflectivity, like pedestrians. We propose the use of Convolutional Autoencoders, in various configurations, to overcome those limitations. Due to its Artificial Neural Network nature, CAE can extract features, learn to identify patterns and recognize moving objects - such as humans and bicycles - while the traditional method acts passively. The results indicate that the usage of CAE overcame CFAR with PD by 76.8%, on average, for reconstructing objects in their correct positions. The results of this work may have many applications, for instance, detecting distant moving objects that are too faint for regular detection.
Original languageEnglish
Title of host publication2020 IEEE International Radar Conference (RADAR)
Number of pages6
ISBN (Electronic)978-1-7281-6813-5
Publication statusPublished - 11 Jun 2020
EventIEEE Radar Conference 2020 - Virtual Conference
Duration: 21 Sept 202025 Sept 2020


ConferenceIEEE Radar Conference 2020
Abbreviated titleRadarConf 2020
CityVirtual Conference


  • Autoencoder
  • Convolutional
  • Deep Learning
  • Doppler-Range
  • Radar
  • 22/2 OA procedure


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