Yazar "Guldogan, Mehmet Burak" seçeneğine göre listele

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    A Bernoulli Filter for Extended Target Tracking Using Random Matrices in a UWB Sensor Network
    (Ieee-Inst Electrical Electronics Engineers Inc, 2016) Eryildirim, Abdulkadir; Guldogan, Mehmet Burak
    In this paper, we propose a new tractable Bernoulli filter based on the random matrix framework to track an extended target in an ultra-wideband (UWB) sensor network. The resulting filter jointly tracks the kinematic and shape parameters of the target and is called the extended target Gaussian inverse Wishart Bernoulli (ET-GIW-Ber) filter. Closed form expressions for the ET-GIW-Ber filter recursions are presented. A clustering step is inserted into the measurement update stage in order to have a computationally tractable filter. In addition, a new method that is consistent with the applied clustering method is embedded into the filter recursions in order to adaptively estimate the time-varying number of measurements of the extended target. The simulation results demonstrate the robust and effective performance of the proposed filter. Furthermore, real data collected from a UWB sensor network are used to assess the performance of the proposed filter. It is shown that the proposed filter yields a very promising performance in estimation of the kinematic and shape parameters of the target.
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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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    Detection of sparse targets with structurally perturbed echo dictionaries
    (Academic Press Inc Elsevier Science, 2013) Guldogan, Mehmet Burak; Arikan, Orhan
    In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel modeling. Performance of standard compressed sensing formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this off-grid problem, we make use of the particle swarm optimization (PSO) to perturb each grid point that reside in each multipath component cluster. Orthogonal matching pursuit (OMP) is used to reconstruct sparse multipath components in a greedy fashion. Extensive simulation results quantify the performance gain and robustness obtained by the proposed algorithm against the off-grid problem faced in sparse multipath channels. (C) 2013 Elsevier Inc. All rights reserved.
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    Enhancements to Threshold Based Range Estimation for Ultra-Wideband Systems
    (Ieee, 2014) Soganci, Hama; Gezici, Sinan; Guldogan, Mehmet Burak
    Ultra-wideband (UWB) signals have very high time resolution, which makes them a very good candidate for range estimation based wireless positioning. Although the accuracy is the major concern for range estimation, it is also important to have low-complexity algorithms that can be employed in real time. In this study, two low-complexity range estimation algorithms are proposed for UWB signals, which achieve improved performance compared to the state-of-the-art low-complexity ranging algorithms. The proposed algorithms are inspired from two well-known algorithms; 'serial backward search' (SBS) and 'jump back and search forward' (JBSF). Performances of the proposed algorithms are compared with those of the SBS and JBSF algorithms based on real measurements. In addition, theoretical bounds are calculated in order to quantify the statistical performance of the algorithms.
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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.
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    Multiperson Tracking With a Network of Ultrawideband Radar Sensors Based on Gaussian Mixture PHD Filters
    (Ieee-Inst Electrical Electronics Engineers Inc, 2015) Gulmezoglu, Berk; Guldogan, Mehmet Burak; Gezici, Sinan
    In this paper, we investigate the use of Gaussian mixture probability hypothesis density filters for multiple person tracking using ultrawideband (UWB) radar sensors in an indoor environment. An experimental setup consisting of a network of UWB radar sensors and a computer is designed, and a new detection algorithm is proposed. The results of this experimental proof-of-concept study show that it is possible to accurately track multiple targets using a UWB radar sensor network in indoor environments based on the proposed approach.