Yazar "Guldogan, Mehmet B." seçeneğine göre listele

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    A Comparative Study on the Performances of the DF Techniques Using a Single Acoustic Vector Sensor
    (Ieee, 2014) Gunes, Ahmet; Guldogan, Mehmet B.; Bereketli, Alper
    Underwater acoustic vector sensors (AVS) are devices which can measure scalar pressure and three dimensional acceleration or particle velocity with only one sensor. Direction of an acoustic target can be estimated by these four measured scalar values. Techniques based on either closed-form expressions or beamforming can be carried out for direction finding by using the axial projections of the gradient vector of the pressure from the target. In this work, the performances of direction of arrival (DOA) estimation techniques for a single underwater AVS are analyzed in detail under the effects of various error sources such as imperfect projections and ambient noise.
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    A Gaussian mixture Bernoulli filter for extended target tracking with application to an ultra-wideband localization system
    (Academic Press Inc Elsevier Science, 2016) Eryildirim, Abdulkadir; Guldogan, Mehmet B.
    This paper presents a Gaussian mixture (GM) implementation of the Bernoulli filter for extended target tracking, which we call the extended target GM Bernoulli (ET-GM-Ber) filter. Closed form expressions for the ET-GM-Ber filter recursions are obtained. A clustering step is integrated into the measurement update stage in order to have a computationally tractable filter. Performance of the proposed filter is tested both on the simulated data and experimental data collected using an ultra-wideband (UWB) localization system. Simulations and experimental results demonstrate the accurate and effective performance of the proposed filter. (C) 2016 Elsevier Inc. All rights reserved.
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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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    Consensus Bernoulli Filter for Distributed Detection and Tracking using Multi-Static Doppler Shifts
    (Ieee-Inst Electrical Electronics Engineers Inc, 2014) Guldogan, Mehmet B.
    In this letter, we study the problem of distributed detection and tracking of a target over a network of separately located Doppler-shift sensors. For this challenging problem, we propose consensus Gaussian mixture - Bernoulli (CGM-Ber) filter. The simulation results prove the robust and effective performance of the proposed approach in a challenging tracking scenario.
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    Experimental Results for Direction of Arrival Estimation with a Single Acoustic Vector Sensor in Shallow Water
    (Wiley, 2015) Bereketli, Alper; Guldogan, Mehmet B.; Kolcak, Taner; Gudu, Tamer; Avsar, Ahmet Levent
    We study the performances of several computationally efficient and simple techniques for estimating direction of arrival (DOA) of an underwater acoustic source using a single acoustic vector sensor (AVS) in shallow water. Underwater AVS is a compact device, which consists of one hydrophone and three accelerometers in a packaged form, measuring scalar pressure and three-dimensional acceleration simultaneously at a single position. A very controlled experimental setup is prepared to test how well-known techniques, namely, arctan-based, intensity-based, time domain beamforming, and frequency domain beamforming methods, perform in estimating DOA of a source in different circumstances. Experimental results reveal that for almost all cases beamforming techniques perform best. Moreover, arctan-based method, which is the simplest of all, provides satisfactory results for practical purposes.
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    Joint underwater target detection and tracking with the Bernoulli filter using an acoustic vector sensor
    (Academic Press Inc Elsevier Science, 2016) Gunes, Ahmet; Guldogan, Mehmet B.
    In this paper, we study the problem of joint underwater target detection and tracking using an acoustic vector sensor (AVS). For this challenging problem, first a realistic frequency domain simulation is set up. The outputs of this simulation generate the two dimensional FRequency-AZimuth (FRAZ) image. On this image, the random finite set (RFS) framework is employed to characterize the target state and sensor measurements. We propose to use the Bernoulli filter, which is the optimal Bayes filter emerged from the RFS framework for randomly on! off switching single dynamic systems. Moreover, to increase the performance of detection and azimuth tracking in low signal-to-noise ratio (SNR) scenarios, a track-before-detect (TBD) measurement model for AVS is proposed to be used with the Bernoulli filter. Sequential Monte Carlo (SMC) implementation is preferred for the Bernoulli filter recursions. Extensive simulation results prove the performance gain obtained by the proposed approach both in estimation accuracy and detection range of the system. (C) 2015 Elsevier Inc. All rights reserved.
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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.
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    Multi-target bearing tracking with a single acoustic vector sensor based on multi-Bernoulli filter
    (Ieee, 2015) Gunes, Ahmet; Guldogan, Mehmet B.
    Underwater acoustic vector sensor (AVS) is a passive sensor which can provide bearing information of the acoustic sources at a single point in space. This is achieved by combining three dimensional acceleration or particle velocity with scalar pressure measurement. In this work, we study the problem of bearing tracking of two targets using the measurements of a single AVS. For this problem, we propose to use the multi-target multi-Bernoulli (MeMBer) filter. The simulation results prove the robust performance of the MeMBer filter.
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    MULTI-TARGET TRACKING USING PASSIVE DOPPLER MEASUREMENTS
    (Ieee, 2013) Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik
    In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
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    Multi-target tracking with PHD filter using Doppler-only measurements
    (Academic Press Inc Elsevier Science, 2014) Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut
    In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation results show that both PHD filter implementations successfully track multiple targets using only Doppler shift measurements. Moreover, as a proof-of-concept, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Experimental study results reveal that it is possible to track multiple ground targets using acoustic Doppler shift measurements in a passive multi-static scenario. We observed that the GM-PHD is more effective, efficient and easy to implement than the SMC-PHD filter. (C) 2014 Elsevier Inc. All rights reserved.