Yazar "Hendeby, Gustaf" seçeneğine göre listele

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    Acoustic Source Localization in a Network of Doppler Shift Sensors
    (Ieee, 2013) Lindgren, David; Guldogan, Mehmet B.; Gustafsson, Fredrik; Habberstad, Hans; Hendeby, Gustaf
    It is well-known that the motion of an acoustic source can be estimated from Doppler shift observations. It is however not obvious how to design a sensor network to efficiently deliver the localization service. In this work a rather simplistic motion model is proposed that is aimed at sensor networks with realistic numbers of sensor nodes. It is also described how to efficiently solve the associated least squares optimization problem by Gauss-Newton variable projection techniques, and how to initiate the numerical search from simple features extracted from the observed frequency series. The methods are demonstrated on real data by determining the distance to a passing propeller-driven aircraft and by localizing an all-terrain vehicle. It is concluded that the processing components included are fairly mature for practical implementations in sensor networks.
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    Knowledge Exploitation for Human Micro-Doppler Classification
    (Ieee-Inst Electrical Electronics Engineers Inc, 2015) Karabacak, Cesur; Gurbuz, Sevgi Z.; Gurbuz, Ali C.; Guldogan, Mehmet B.; Hendeby, Gustaf; Gustafsson, Fredrik
    Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.