On model, algorithms and experiment for micro-doppler based recognition of ballistic targets

Adriano Rosario Persico, Carmine Clemente, Domenico Gaglione, Christos V. Ilioudis, Jianlin Cao, Luca Pallotta, Antonio De Maio, Ian Proudler, John J. Soraghan

Research output: Contribution to journalArticlepeer-review

84 Citations (Scopus)
91 Downloads (Pure)


The ability to discriminate between Ballistic Missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defence system can intercept the missile is very short with respect to target velocities, it is fundamental to minimise the number of shoots per kill. For this reason a reliable technique to classify warheads and confusing objects is required. In the efficient warhead classification system presented in this paper a model and a robust framework is developed, which incorporates different microDoppler based classification techniques. The reliability of the proposed framework is tested on both simulated and real data
Original languageEnglish
Pages (from-to)1088-1108
Number of pages21
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number3
Early online date7 Feb 2017
Publication statusPublished - 30 Jun 2017


  • ballistic missiles classification
  • micro-doppler based classification
  • cadence velocity diagram
  • pseudo-Zernike moments
  • Gabor filters
  • ACVD based feature vector approach
  • pseudo-Zernike based feature vector approach


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