Yazar "Gunes, Ahmet" 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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    Analysis of Direction Finding Techniques Using a Single Underwater Acoustic Vector Sensor
    (Ieee, 2014) Gunes, Ahmet; Bereketli, Alper; Guldogan, M. Burak
    Underwater acoustic vector sensors (AN'S) are devices which can measure scalar pressure and three dimensional acceleration or particle velocity with only one sensor. Bearing estimation for the target can be accomplished by these four measured scalar values. Algorithms based on 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 estimation algorithms for underwater acoustic vector sensors are analyzed in detail under the effects of various error sources such as imperfect projections and ambient noise.
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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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    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.