Model-based sparse recovery method for automatic classification of helicopters

Carmine Clemente, Domenico Gaglione, Fraser Kenneth Coutts, Gang Li, John Soraghan

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

13 Citations (Scopus)

Abstract

The rotation of rotor blades of a helicopter induces a Doppler modulation around the main Doppler shift. Such a non-stationary modulation, commonly called micro-Doppler signature, can be used to perform classification of the target. In this paper a model-based automatic helicopter classification algorithm is presented. A sparse signal model for radar return from a helicopter is developed and by means of the theory of sparse signal recovery, the characteristic parameters of the target are extracted and used for the classification. This approach does not require any learning process of a training set or adaptive processing of the received signal. Moreover, it is robust with respect to the initial position of the blades and the angle that the LOS forms with the perpendicular to the plane on which the blades lie. The proposed approach is tested on simulated and real data.
LanguageEnglish
Title of host publication2015 IEEE Radar Conference (RadarCon)
PublisherIEEE
Pages1161-1165
Number of pages5
ISBN (Print)978-1-4799-8231-8
DOIs
Publication statusPublished - 22 Jun 2015
EventIEEE International Radar Conference 2015 - Arlington, United States
Duration: 11 May 201515 May 2015

Conference

ConferenceIEEE International Radar Conference 2015
CountryUnited States
CityArlington
Period11/05/1515/05/15

Fingerprint

Helicopters
Recovery
Modulation
Helicopter rotors
Doppler effect
Turbomachine blades
Radar
Processing

Keywords

  • helicopter classification
  • recovery method
  • automatic classification

Cite this

Clemente, C., Gaglione, D., Coutts, F. K., Li, G., & Soraghan, J. (2015). Model-based sparse recovery method for automatic classification of helicopters. In 2015 IEEE Radar Conference (RadarCon) (pp. 1161-1165). IEEE. https://doi.org/10.1109/RADAR.2015.7131169
Clemente, Carmine ; Gaglione, Domenico ; Coutts, Fraser Kenneth ; Li, Gang ; Soraghan, John. / Model-based sparse recovery method for automatic classification of helicopters. 2015 IEEE Radar Conference (RadarCon). IEEE, 2015. pp. 1161-1165
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title = "Model-based sparse recovery method for automatic classification of helicopters",
abstract = "The rotation of rotor blades of a helicopter induces a Doppler modulation around the main Doppler shift. Such a non-stationary modulation, commonly called micro-Doppler signature, can be used to perform classification of the target. In this paper a model-based automatic helicopter classification algorithm is presented. A sparse signal model for radar return from a helicopter is developed and by means of the theory of sparse signal recovery, the characteristic parameters of the target are extracted and used for the classification. This approach does not require any learning process of a training set or adaptive processing of the received signal. Moreover, it is robust with respect to the initial position of the blades and the angle that the LOS forms with the perpendicular to the plane on which the blades lie. The proposed approach is tested on simulated and real data.",
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Clemente, C, Gaglione, D, Coutts, FK, Li, G & Soraghan, J 2015, Model-based sparse recovery method for automatic classification of helicopters. in 2015 IEEE Radar Conference (RadarCon). IEEE, pp. 1161-1165, IEEE International Radar Conference 2015, Arlington, United States, 11/05/15. https://doi.org/10.1109/RADAR.2015.7131169

Model-based sparse recovery method for automatic classification of helicopters. / Clemente, Carmine; Gaglione, Domenico; Coutts, Fraser Kenneth; Li, Gang; Soraghan, John.

2015 IEEE Radar Conference (RadarCon). IEEE, 2015. p. 1161-1165.

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

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PY - 2015/6/22

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N2 - The rotation of rotor blades of a helicopter induces a Doppler modulation around the main Doppler shift. Such a non-stationary modulation, commonly called micro-Doppler signature, can be used to perform classification of the target. In this paper a model-based automatic helicopter classification algorithm is presented. A sparse signal model for radar return from a helicopter is developed and by means of the theory of sparse signal recovery, the characteristic parameters of the target are extracted and used for the classification. This approach does not require any learning process of a training set or adaptive processing of the received signal. Moreover, it is robust with respect to the initial position of the blades and the angle that the LOS forms with the perpendicular to the plane on which the blades lie. The proposed approach is tested on simulated and real data.

AB - The rotation of rotor blades of a helicopter induces a Doppler modulation around the main Doppler shift. Such a non-stationary modulation, commonly called micro-Doppler signature, can be used to perform classification of the target. In this paper a model-based automatic helicopter classification algorithm is presented. A sparse signal model for radar return from a helicopter is developed and by means of the theory of sparse signal recovery, the characteristic parameters of the target are extracted and used for the classification. This approach does not require any learning process of a training set or adaptive processing of the received signal. Moreover, it is robust with respect to the initial position of the blades and the angle that the LOS forms with the perpendicular to the plane on which the blades lie. The proposed approach is tested on simulated and real data.

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Clemente C, Gaglione D, Coutts FK, Li G, Soraghan J. Model-based sparse recovery method for automatic classification of helicopters. In 2015 IEEE Radar Conference (RadarCon). IEEE. 2015. p. 1161-1165 https://doi.org/10.1109/RADAR.2015.7131169