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    Classification of Human Micro-Doppler in a Radar Network
    (Ieee, 2013) Tekeli, Burkan; Gurbuz, Sevgi Zubeyde; Yuksel, Melda; Gurbuz, Ali Cafer; Guldogan, Mehmet Burak
    The unique, bi-pedal motion of humans has been shown to generate a characteristic micro-Doppler signature in the time-frequency domain that can be used to discriminate humans from not just other targets, but also between different activities, such as walking and running. However, the classification performance increasingly drops as the aspect angle between the target and radar approaches perpendicular, and the radial velocity component seen by the radar is minimized. In this paper, exploitation of the multi-static micro-Doppler signature formed from multi-angle observations of a radar network is proposed to improve oblique-angle classification performance. The concept of mutual information is applied to find the order of importance of features for a given classification problem, thereby enabling the selection of optimal features prior to classification. Strategies for fusing multistatic data using mutual information and model-based approaches are discussed.
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    Importance Ranking of Features for Human Micro-Doppler Classification with a Radar Network
    (Ieee, 2013) Gurbuz, Sevgi Zubeyde; Tekeli, Burkan; Yuksel, Melda; Karabacak, Cesur; Gurbuz, Ali Cafer; Guldogan, Mehmet Burak
    Over the past decade, the human micro-Doppler signature has been a subject of intense research. In particular, much work has been done in relation to computing features for use in a variety of classification problems, such as arm swing detection, activity classification, and target identification. Although dozens of features have been proposed for these purposes, little work has examined the issue of which features are more important - i.e., have a greater impact on classification performance - than others. In this work, an information theoretic approach is applied to compute the importance ranking of features prior to classification for the specific problem of discriminating human walking from running. Results show that the ranking of features according to mutual information directly relates to classification performance using support vector machines.