Browsing by Author "Guldogan, M. B."

Now showing 1 - 6 of 6

Open Access
Cross-ambiquity function domain multipath channel parameter estimation
(Elsevier, 2011-11-23) Guldogan, M. B.; Arıkan, Orhan
A new array signal processing technique is proposed to estimate the direction-of-arrivals (DOAs), time delays, Doppler shifts and amplitudes of a known waveform impinging on an array of antennas from several distinct paths. The proposed technique detects the presence of multipath components by integrating cross-ambiguity functions (CAF) of array outputs, hence, it is called as the cross-ambiguity function direction finding (CAF-DF). The performance of the CAF-DF technique is compared with the space-alternating generalized expectation-maximization (SAGE) and the multiple signal classification (MUSIC) techniques as well as the Cramer-Rao lower bound. The CAF-DF technique is found to be superior in terms of root-mean-squared-error (rMSE) to the SAGE and MUSIC techniques. (
Open Access
Detection of sparse targets with structurally perturbed echo dictionaries
(Elsevier, 2013) Guldogan, M. B.; Arıkan, 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.
Open Access
Multipath channel identification by using global optimization in ambiguity function domain
(ELSEVIER, 2011-06-15) Guldogan, M. B.; Arıkan, Orhan
A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delayDoppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delayDoppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delayDoppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.
Open Access
Multipath separation-direction of arrival (MS-DOA) with genetic search algorithm for HF channels
(ELSEVIER, 2009) Arikan, F.; Koroglu, O.; Fidan, S.; Arıkan, Orhan; Guldogan, M. B.
Direction-of-Arrival (DOA) defines the estimation of arrival angles of an electromagnetic wave impinging on a set of sensors. For dispersive and time-varying HF channels, where the propagating wave also suffers from the multipath phenomena, estimation of DOA is a very challenging problem. Multipath Separation-Direction of Arrival (MS-DOA), that is developed to estimate both the arrival angles in elevation and azimuth and the incoming signals at the output of the reference antenna with very high accuracy, proves itself as a strong alternative in DOA estimation for HF channels. In MS-DOA, a linear system of equations is formed using the coefficients of the basis vector for the array output vector, the incoming signal vector and the array manifold. The angles of arrival in elevation and azimuth are obtained as the maximizers of the sum of the magnitude squares of the projection of the signal coefficients on the column space of the array manifold. In this study, alternative Genetic Search Algorithms (GA) for the maximizers of the projection sum are investigated using simulated and experimental ionospheric channel data. It is observed that GA combined with MS-DOA is a powerful alternative in online DOA estimation and can be further developed according to the channel characteristics of a specific HF link.
Open Access
Multiperson tracking with a network of ultrawideband radar sensors based on gaussian mixture PHD filters
(Institute of Electrical and Electronics Engineers Inc., 2015) Gulmezoglu, B.; Guldogan, M. B.; 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. © 2014 IEEE.
Open Access
A new technique for direction of arrival estimation for ionospheric multipath channels
(ELSEVIER, 2009) Guldogan, M. B.; Arıkan, Orhan; Arikan, F.
A novel array signal processing technique is proposed to estimate HF channel parameters including number of paths, their respective direction of arrivals (DOA), delays, Doppler shifts and amplitudes. The proposed technique utilizes the Cross Ambiguity Function (CAF), hence, called as the CAF-DF technique. The CAF-DF technique iteratively processes the array output data and provides reliable estimates for DOA, delay, Doppler shift and amplitude corresponding to each impinging HF propagated wave onto an antenna array. Obtained results for both real and simulated data at different signal to noise ratio (SNR) values indicate the superior performance of the proposed technique over the well known MUltiple SIgnal Classification (MUSIC) technique.