Helicopter classification via period estimation and time-frequency masks

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Zhang, Rui and Li, Gang and Clemente, Carmine and Varshney, Pramod K. (2015) Helicopter classification via period estimation and time-frequency masks. In: 6th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015), 2015-12-13 - 2015-12-16. (https://doi.org/10.1109/CAMSAP.2015.7383736)

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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.

ORCID iDs

Zhang, Rui, Li, Gang, Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X and Varshney, Pramod K.;
  • Item type:Conference or Workshop Item(Paper)
    ID code:57204
    Dates:
    Date
    Event
    December 2015
    Published
    October 2015
    Accepted
    Notes:(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
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:01 Aug 2016 10:39
    Last modified:11 Nov 2024 16:47
    URI:https://strathprints.strath.ac.uk/id/eprint/57204
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