Label consistent K-SVD for sparse micro-doppler classification

Fraser K. Coutts, Domenico Gaglione, Carmine Clemente, Gang Li, Ian K. Proudler, John J. Soraghan

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

10 Citations (Scopus)


Secondary motions of targets observed by radar introduce non-stationary returns containing the so-called micro-Doppler information. This is characterizing information that can be exploited to enhance automatic target recognition systems. In this paper, the challenge of classifying the micro-Doppler return of helicopters is addressed. A robust dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD), is applied to identify effectively and efficiently helicopters. The effectiveness of the proposed algorithm is demonstrated on both synthetic and real radar data.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Digital Signal Processing (DSP)
Number of pages5
ISBN (Print)978-1-4799-8058-1
Publication statusPublished - Jul 2015
Event2015 IEEE International Conference on Digital Signal Processing (DSP) - Singapore, Singapore, United Kingdom
Duration: 21 Jul 201524 Jul 2015


Conference2015 IEEE International Conference on Digital Signal Processing (DSP)
Country/TerritoryUnited Kingdom


  • doppler radar
  • airborne radar
  • helicopters
  • radar signal processing
  • radar target recognition
  • singular value decomposition
  • automatic target recognition systems
  • label consistent K-SVD
  • microdoppler information
  • microdoppler return
  • radar data
  • robust dictionary learning algorithm
  • secondary motions
  • sparse microDoppler classification
  • accuracy
  • blades
  • classification algorithms
  • dictionaries
  • radar
  • training


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