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    Gridless quadrature compressive sampling with interpolated array technique

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    1503.00436v1.pdf
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    Genre
    Pre-print
    Date
    2017-04-01
    Author
    Xi, F
    Chen, S
    Zhang, YD
    Liu, Z
    Subject
    Compressed sensing
    Quadrature sampling
    Beamspace DOA estimation
    Interpolated array
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/4918
    
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    DOI
    10.1016/j.sigpro.2016.10.010
    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.
    Citation to related work
    Elsevier BV
    Has part
    Signal Processing
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    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/4900
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