A complexity-reduced ML parametric signal reconstruction method

Simple item page
dc.citation.volumeNumber2011en_US
dc.contributor.authorDeprem, Z.en_US
dc.contributor.authorLeblebicioglu, K.en_US
dc.contributor.authorArkan O.en_US
dc.contributor.authorÇetin, A.E.en_US
dc.date.accessioned2016-02-08T09:52:27Z
dc.date.available2016-02-08T09:52:27Z
dc.date.issued2011en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThe problem of component estimation from a multicomponent signal in additive white Gaussian noise is considered. A parametric ML approach, where all components are represented as a multiplication of a polynomial amplitude and polynomial phase term, is used. The formulated optimization problem is solved via nonlinear iterative techniques and the amplitude and phase parameters for all components are reconstructed. The initial amplitude and the phase parameters are obtained via time-frequency techniques. An alternative method, which iterates amplitude and phase parameters separately, is proposed. The proposed method reduces the computational complexity and convergence time significantly. Furthermore, by using the proposed method together with Expectation Maximization (EM) approach, better reconstruction error level is obtained at low SNR. Though the proposed method reduces the computations significantly, it does not guarantee global optimum. As is known, these types of non-linear optimization algorithms converge to local minimum and do not guarantee global optimum. The global optimum is initialization dependent. © 2011 Z. Deprem et al.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T09:52:27Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2011en
dc.identifier.doi10.1155/2011/875132en_US
dc.identifier.issn16876172
dc.identifier.urihttp://hdl.handle.net/11693/21882
dc.language.isoEnglishen_US
dc.relation.isversionofhttp://dx.doi.org/10.1155/2011/875132en_US
dc.source.titleEurasip Journal on Advances in Signal Processingen_US
dc.subjectAdditive White Gaussian noiseen_US
dc.subjectAlternative methodsen_US
dc.subjectComponent estimationen_US
dc.subjectConvergence timeen_US
dc.subjectExpectation-maximization approachesen_US
dc.subjectGlobal optimumen_US
dc.subjectIterative techniqueen_US
dc.subjectLocal minimumsen_US
dc.subjectLow SNRen_US
dc.subjectMulticomponent signalsen_US
dc.subjectNon-linear optimization algorithmsen_US
dc.subjectOptimization problemsen_US
dc.subjectPhase parametersen_US
dc.subjectPolynomial phaseen_US
dc.subjectReconstruction erroren_US
dc.subjectTime-frequency techniquesen_US
dc.subjectGaussian noise (electronic)en_US
dc.subjectIterative methodsen_US
dc.subjectOptimizationen_US
dc.subjectWhite noiseen_US
dc.subjectComputational complexityen_US
dc.titleA complexity-reduced ML parametric signal reconstruction methoden_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A complexity-reduced ML parametric signal reconstruction method.pdf
Size:
715.42 KB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download

Collections

Department of Electrical and Electronics Engineering