Cross-ambiguity function domain multipath channel parameter estimation

Güldoǧan, Mehmet Burak; Arıkan, Orhan

doi:10.1016/j.dsp.2011.11.002

Cited by 3 publications

(2 citation statements)

References 38 publications

(53 reference statements)

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“…CAF calculation is the optimal solution for detection, in the case of one multipath component. If there exist two or more multipath components separated enough in delay-Doppler domain, again, CAF surface offers a very useful detection surface by using properly chosen waveforms for the application of interest [45][46][47][48]. Proposed algorithm starts with the detection of multipath clusters on ambiguity surface exceeding a predefined detection threshold.…”

Section: Sparse Approximation On Cross-ambiguity Function Surfacementioning

confidence: 99%

Detection of sparse targets with structurally perturbed echo dictionaries

Güldoǧan

Arıkan

2013

Digital Signal Processing

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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. © 2013 Elsevier Inc

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Section: Sparse Approximation On Cross-ambiguity Function Surfacementioning

confidence: 99%

Detection of sparse targets with structurally perturbed echo dictionaries

Güldoǧan

Arıkan

2013

Digital Signal Processing

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“…In [9,20], the authors utilize compressive sensing and sparse representation [48][49][50][51][52] to resolve the two-dimensional DOA estimation problem. Several two-dimensional DOA estimation algorithms based on the L-shaped planar array (with a larger aperture) are proposed [12,19,20,25,32,33,35,36], which have low computational complexity. In [30], the auto-correlation and cross-correlation information is used, and an efficient algorithm is proposed to deal with the DOA estimation problem.…”

Section: Introductionmentioning

confidence: 99%

An Efficient MUSIC Algorithm Enhanced by Iteratively Estimating Signal Subspace and Its Applications in Spatial Colored Noise

Zhang

Feng

2022

Remote Sensing

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The classical multiple signal classification (MUSIC) algorithms mainly have two limitations. One is an insufficient number of snapshots, which usually causes an ill-posed sample covariance matrix in many real applications. The other limitation is the intense space-colored and time-white noise, which also breaks the separability between signal and noise subspaces. In the case of the insufficient sample, there are few signal components in the non-zero delay sample covariance matrix (SCM), where the space-colored and time-white noise components are suppressed by the temporal method. A set of non-zero delay sample covariance matrices are constructed, and a nonlinear object function is formulated. Hence, the sufficient non-zero delay SCMs ensure that enough signal components are used for signal subspace estimation. Then, the constrained optimization problem is converted into an unconstrained one by exploiting the Lagrange multiplier method. The nonlinear equation is solved by Newton’s method iteratively. Moreover, a proper initial value of the new algorithm is given, which can improve the convergence of the iterative algorithm. In this paper, the noise subspace is removed by the pre-projection technique in every iteration step. Then, an improved signal subspace is obtained, and a more efficient MUSIC algorithm is proposed. Experimental results show that the proposed algorithm achieves significantly better performance than the existing methods.

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