Browsing by Subject "Cross-ambiguity function"
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Item Open Access Compressed sensing on ambiguity function domain for high resolution detection(IEEE, 2010) Güldoǧan, Mehmet B.; Pilancı, Mert; Arıkan, OrhanIn this paper, by using compressed sensing techniques, a new approach to achieve robust high resolution detection in sparse multipath channels is presented. Currently used sparse reconstruction techniques are not immediately applicable in wireless channel modeling and radar signal processing. Here, we make use of the cross-ambiguity function (CAF) and transformed the reconstruction problem from time to delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of the multipath components. Simulation results quantify the performance gain and robustness obtained by this new CAF based compressed sensing approach. ©2010 IEEE.Item Open Access Detection of sparse targets with structurally perturbed echo dictionaries(Elsevier, 2013) Guldogan, M. B.; Arıkan, OrhanIn 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.Item Open Access Particle swarm optimization based channel identification in cross-ambiguity domain(IEEE, 2010) Güldoğan, Mehmet Burak; Arıkan, OrhanIn this paper, a new array signal processing technique by using particle swarm optimization (PSO) is proposed to identify multipath channel parameters. The proposed technique provides estimates to the channel parameters by finding a global minimum of an optimization problem. Since the optimization problem is formulated in the cross-ambiguity function (CAF) domain of the transmitted signal and the received array outputs, the proposed technique is called as PSO-CAF. The performance of the PSO-CAF is compared with the space alternating generalized expectation maximization (SAGE) technique and with another recently proposed PSO based technique for various SNR values. Simulation results indicate the superior performance of the PSO-CAF technique over mentioned techniques for all SNR values. ©2010 IEEE.