Helicopter classification via period estimation and time-frequency masks

Rui Zhang, Gang Li, Carmine Clemente, Pramod K. Varshney

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

The rotation of blades of a helicopter induces a Doppler modulation around the main Doppler shift, which is commonly called the micro-Doppler signature and can be used for target classification. In this paper, an automatic helicopter
classification method is proposed by estimating the period of the micro-Doppler signature and identifying the number of blades via time-frequency masks. The advantages of this method are threefold: (1) it determines the number of blades automatically; (2) it significantly reduces the computational burden compared to the classical model dictionary-based classification methods; (3) it is robust with respect to the inclination of the helicopter. The effectiveness of the proposed approach is validated by using both synthetic and real data.

Workshop

Workshop6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015)
CountryMexico
CityCancun
Period13/12/1516/12/15

Fingerprint

Helicopters
Masks
Doppler effect
Glossaries
Modulation

Keywords

  • micro-doppler
  • helicopter classification
  • time-frequency analysis
  • radar signal processing
  • signal classification
  • doppler radar

Cite this

Zhang, R., Li, G., Clemente, C., & Varshney, P. K. (2015). Helicopter classification via period estimation and time-frequency masks. 61-64. Paper presented at 6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015), Cancun, Mexico. https://doi.org/10.1109/CAMSAP.2015.7383736
Zhang, Rui ; Li, Gang ; Clemente, Carmine ; Varshney, Pramod K. / Helicopter classification via period estimation and time-frequency masks. Paper presented at 6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015), Cancun, Mexico.4 p.
@conference{e3211d37a7ac4137988ab13332567f89,
title = "Helicopter classification via period estimation and time-frequency masks",
abstract = "The rotation of blades of a helicopter induces a Doppler modulation around the main Doppler shift, which is commonly called the micro-Doppler signature and can be used for target classification. In this paper, an automatic helicopterclassification method is proposed by estimating the period of the micro-Doppler signature and identifying the number of blades via time-frequency masks. The advantages of this method are threefold: (1) it determines the number of blades automatically; (2) it significantly reduces the computational burden compared to the classical model dictionary-based classification methods; (3) it is robust with respect to the inclination of the helicopter. The effectiveness of the proposed approach is validated by using both synthetic and real data.",
keywords = "micro-doppler, helicopter classification, time-frequency analysis, radar signal processing, signal classification, doppler radar",
author = "Rui Zhang and Gang Li and Carmine Clemente and Varshney, {Pramod K.}",
note = "(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works; 6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015) ; Conference date: 13-12-2015 Through 16-12-2015",
year = "2015",
month = "12",
doi = "10.1109/CAMSAP.2015.7383736",
language = "English",
pages = "61--64",

}

Zhang, R, Li, G, Clemente, C & Varshney, PK 2015, 'Helicopter classification via period estimation and time-frequency masks' Paper presented at 6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015), Cancun, Mexico, 13/12/15 - 16/12/15, pp. 61-64. https://doi.org/10.1109/CAMSAP.2015.7383736

Helicopter classification via period estimation and time-frequency masks. / Zhang, Rui; Li, Gang; Clemente, Carmine; Varshney, Pramod K.

2015. 61-64 Paper presented at 6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015), Cancun, Mexico.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Helicopter classification via period estimation and time-frequency masks

AU - Zhang, Rui

AU - Li, Gang

AU - Clemente, Carmine

AU - Varshney, Pramod K.

N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

PY - 2015/12

Y1 - 2015/12

N2 - The rotation of blades of a helicopter induces a Doppler modulation around the main Doppler shift, which is commonly called the micro-Doppler signature and can be used for target classification. In this paper, an automatic helicopterclassification method is proposed by estimating the period of the micro-Doppler signature and identifying the number of blades via time-frequency masks. The advantages of this method are threefold: (1) it determines the number of blades automatically; (2) it significantly reduces the computational burden compared to the classical model dictionary-based classification methods; (3) it is robust with respect to the inclination of the helicopter. The effectiveness of the proposed approach is validated by using both synthetic and real data.

AB - The rotation of blades of a helicopter induces a Doppler modulation around the main Doppler shift, which is commonly called the micro-Doppler signature and can be used for target classification. In this paper, an automatic helicopterclassification method is proposed by estimating the period of the micro-Doppler signature and identifying the number of blades via time-frequency masks. The advantages of this method are threefold: (1) it determines the number of blades automatically; (2) it significantly reduces the computational burden compared to the classical model dictionary-based classification methods; (3) it is robust with respect to the inclination of the helicopter. The effectiveness of the proposed approach is validated by using both synthetic and real data.

KW - micro-doppler

KW - helicopter classification

KW - time-frequency analysis

KW - radar signal processing

KW - signal classification

KW - doppler radar

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DO - 10.1109/CAMSAP.2015.7383736

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Zhang R, Li G, Clemente C, Varshney PK. Helicopter classification via period estimation and time-frequency masks. 2015. Paper presented at 6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015), Cancun, Mexico. https://doi.org/10.1109/CAMSAP.2015.7383736