MUSIC Imaging Using Phase-Space Gaussian-Beams Processing

• Published in: IEEE transactions on antennas and propagation Vol. 62; no. 3; pp. 1270 - 1281
• Main Authors: Heilpern, Tal, Heyman, Ehud
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Approximation methods; Arrays; Artificial intelligence; Beam summation method; beam-based data processing; Beams (radiation); Computer science; control theory; systems; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Exact solutions; Filtering; Gaussian beams; Imaging; Information, signal and communications theory; inverse scattering; Multiple signal classification; MUSIC algorithm; Noise measurement; Pattern recognition. Digital image processing. Computational geometry; Receivers; Scattering; Signal and communications theory; Signal, noise; Telecommunications and information theory; Transformations; Vectors; Backpropagation; Computer vision; Filtering; Data transformation; Data processing; inverse scattering; beam-based data processing; MUSIC algorithm; Experimental study; Gaussian beams; Green function; Modeling; Exact solution; Excitation source; Backpropagation algorithm; Gaussian process; Imaging; Arrival angle; Domain decomposition; Phase space; Summation; Beam summation method; Multiple classification; Point source
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2013.2295614

In a recent paper we presented a beam-based backpropagation and correlation imaging scheme for targets in a homogeneous medium. This beam-based approach is extended here for MUSIC-imaging algorithms. In the beam approach, the fields of the physical arrays of point-sources and point receivers are expanded using special sets of collimated beam-sources and beam-receivers. This converts the physical scattering data into a beam-domain data, describing the scattering amplitudes seen (synthetically) by receiver beams due to excitation by source beams. The image is then formed by applying the MUSIC algorithm directly in the beam domain. We derive a closed form expression for the data transformation to the beam domain and then derive multi-experiments MUSIC-imaging algorithms that accommodate the measurement noise. The beam approach enables local imaging of any given sub-domain of interest by retaining only the subset of beams that pass through that domain, thus reducing the overall computation complexity relative to the conventional Green function approach, and filtering out of data and noise that arrive from non-relevant directions. Numerical investigations reveal that the beam approach also provides a better resolution under low S/N conditions.