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  • 1.
    Bjorklund, Svante
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Petersson, Henrik
    Swedish Def Res Agcy FOI, SE-58111 Linkoping, Sweden..
    Hendeby, Gustaf
    Swedish Def Res Agcy FOI, SE-58111 Linkoping, Sweden.;Linkoping Univ, SE-58183 Linkoping, Sweden..
    Features for micro-Doppler based activity classification2015In: IET radar, sonar & navigation, ISSN 1751-8784, E-ISSN 1751-8792, Vol. 9, no 9, p. 1181-1187Article in journal (Refereed)
    Abstract [en]

    Safety and security applications benefit from better situational awareness. Radar micro-Doppler signatures from an observed target carry information about the target's activity, and have potential to improve situational awareness. This article describes, compares, and discusses two methods to classify human activity based on radar micro-Doppler data. The first method extracts physically interpretable features from the time-velocity domain such as the main cycle time and properties of the envelope of the micro-Doppler spectra and use these in the classification. The second method derives its features based on the components with the most energy in the cadence-velocity domain (obtained as the Fourier transform of the time-velocity domain). Measurements from a field trial show that the two methods have similar activity classification performance. It is suggested that target base velocity and main limb cadence frequency are indirect features of both methods, and that they do often alone suffice to discriminate between the studied activities. This is corroborated by experiments with a reduced feature set. This opens up for designing new more compact feature sets. Moreover, weaknesses of the methods and the impact of non-radial motion are discussed.

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