Guldogan, M. B.Arıkan, Orhan2016-02-082016-02-0820131051-2004http://hdl.handle.net/11693/20838In 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.EnglishChannel identificationCompressed sensing (CS)Cross-ambiguity function (CAF)Orthogonal matching pursuit (OMP)Particle swarm optimization (PSO)Sparse approximationChannel identificationCompressive sensingCross-ambiguity functionOrthogonal matching pursuitSparse approximationsAlgorithmsParticle swarm optimization (PSO)Signal reconstructionMultipath propagationArticle10.1016/j.dsp.2013.03.003