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dc.creatorXi, F
dc.creatorChen, S
dc.creatorZhang, YD
dc.creatorLiu, Z
dc.date.accessioned2021-01-22T21:51:45Z
dc.date.available2021-01-22T21:51:45Z
dc.date.issued2017-04-01
dc.identifier.issn0165-1684
dc.identifier.issn1872-7557
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4900
dc.identifier.otherEH8TF (isidoc)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4918
dc.description.abstract© 2016 Elsevier B.V. Quadrature compressive sampling (QuadCS) is a sub-Nyquist sampling scheme for acquiring in-phase and quadrature (I/Q) components in radar. In this scheme, the received intermediate frequency (IF) signals are expressed as a linear combination of time-delayed and scaled replicas of the transmitted waveforms. For sparse IF signals on discrete grids of time-delay space, the QuadCS can efficiently reconstruct the I/Q components from sub-Nyquist samples. In practice, the signals are characterized by a set of unknown time-delay parameters in a continuous space. Then conventional sparse signal reconstruction will deteriorate the QuadCS reconstruction performance. This paper focuses on the reconstruction of the I/Q components with continuous delay parameters. A parametric spectrum-matched dictionary is defined, which sparsely describes the IF signals in the frequency domain by delay parameters and gain coefficients, and the QuadCS system is reexamined under the new dictionary. With the inherent structure of the QuadCS system, it is found that the estimation of delay parameters can be decoupled from that of sparse gain coefficients, yielding a beamspace direction-of-arrival (DOA) estimation formulation with a time-varying beamforming matrix. Then an interpolated beamspace DOA method is developed to perform the DOA estimation. An optimal interpolated array is established and sufficient conditions to guarantee the successful estimation of the delay parameters are derived. With the estimated delays, the gain coefficients can be conveniently determined by solving a linear least-squares problem. Extensive simulation results evidently demonstrate the superiority of the proposed algorithms in achieving super-resolution time-delay estimation and high-accuracy sparse signal reconstruction.
dc.format.extent1-12
dc.language.isoen
dc.relation.haspartSignal Processing
dc.relation.isreferencedbyElsevier BV
dc.rightsAll Rights Reserved
dc.subjectCompressed sensing
dc.subjectQuadrature sampling
dc.subjectBeamspace DOA estimation
dc.subjectInterpolated array
dc.titleGridless quadrature compressive sampling with interpolated array technique
dc.typeArticle
dc.type.genrePre-print
dc.relation.doi10.1016/j.sigpro.2016.10.010
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidZhang, Yimin Daniel|0000-0002-4625-209X
dc.date.updated2021-01-22T21:51:42Z
refterms.dateFOA2021-01-22T21:51:45Z


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