Time-reversal multiple signal classification in case of noise: a phase-coherent approach.

The problem of locating point-like targets beyond the classical resolution limit is revisited. Although time-reversal MUltiple SIgnal Classification (MUSIC) is known for its super-resolution ability in localization of point scatterers, in the presence of noise this super-resolution property will easily break down. In this paper a phase-coherent version of time-reversal MUSIC is proposed, which can overcome this fundamental limit. The algorithm has been tested employing synthetic multiple scattering data based on the Foldy-Lax model, as well as experimental ultrasound data acquired in a water tank. Using a limited frequency band, it was demonstrated that the phase-coherent MUSIC algorithm has the potential of giving significantly better resolved scatterer locations than standard time-reversal MUSIC.

[1]  Leiv-J. Gelius,et al.  Diffraction‐limited imaging and beyond – the concept of super resolution ‡ , 2011 .

[2]  A. Devaney,et al.  Time-reversal imaging with multiple signal classification considering multiple scattering between the targets , 2004 .

[3]  Hongkai Zhao,et al.  A phase and space coherent direct imaging method. , 2009, The Journal of the Acoustical Society of America.

[4]  A. Austeng,et al.  Adaptive Beamforming Applied to Medical Ultrasound Imaging , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[5]  Leiv-J. Gelius Time‐reversal and super‐resolution in case of two transceiver arrays: Generalized DORT , 2009 .

[6]  J. Devaney,et al.  1 Super-resolution Processing of Multi-static Data Using Time Reversal and MUSIC A , 2000 .

[7]  Francesco Simonetti,et al.  Illustration of the role of multiple scattering in subwavelength imaging from far-field measurements. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  Claire Prada,et al.  Experimental subwavelength localization of scatterers by decomposition of the time reversal operator interpreted as a covariance matrix. , 2003, The Journal of the Acoustical Society of America.

[9]  Sean K Lehman,et al.  Transmission mode time-reversal super-resolution imaging. , 2003, The Journal of the Acoustical Society of America.

[10]  M. Viberg,et al.  Two decades of array signal processing research: the parametric approach , 1996, IEEE Signal Process. Mag..

[11]  Jean-Gabriel Minonzio,et al.  INFLUENCE OF NOISE ON SUBWAVELENGTH IMAGING OF TWO CLOSE SCATTERERS USING TIME REVERSAL METHOD: THEORY AND EXPERIMENTS , 2009 .

[12]  Richard J. Vaccaro,et al.  A Second-Order Perturbation Expansion for the SVD , 1994 .

[13]  A.J. Devaney Time reversal imaging of obscured targets from multistatic data , 2005, IEEE Transactions on Antennas and Propagation.

[14]  S.S. Reddi,et al.  Multiple Source Location-A Digital Approach , 1979, IEEE Transactions on Aerospace and Electronic Systems.

[15]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[16]  M. S. Babtlett Smoothing Periodograms from Time-Series with Continuous Spectra , 1948, Nature.

[17]  Hongkai Zhao,et al.  A direct imaging method using far-field data* , 2007 .