Neural Network-Based Multitarget Detection Within Correlated Heavy-Tailed Clutter

Research output: Contribution to journalArticlepeer-review


This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified neural network (NN) model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the NN training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CFAR), the trimmed-mean CFAR, and the adaptive normalized matched-filter detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.

Original languageEnglish
Pages (from-to)5684-5698
Number of pages15
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number5
StatePublished - 5 Apr 2023


  • Adaptive normalized matched-filter (ANMF)
  • cell-averaging constant false-alarm rate (CA-CFAR)
  • correlated heavy-tailed clutter
  • deep learning
  • linear frequency modulated (LFM)
  • machine learning (ML)
  • multiple target detection
  • neural networks (NNs)
  • radar target detection
  • range-Doppler
  • trimmed-mean CFAR (TM-CFAR)

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering


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