DOA estimation based on compressive sampling array with novel beamforming

Based on the sparse property of the targets distributed in spatial domain, a novel Compressive Sensing Beamforming (CSB) algorithm based on compressive sampling array (CSA) is proposed for DOA estimation. A new compression matrix is designed for CSA to be able to compress a large size array into small size array that brings the advantage of reducing both hardware and software complexity, and the CSB can be viewed as a combination of merit of conventional Capon and MUSIC method which not only skips sources number estimation and EVD, but also behaves satisfactorily with a few snapshot. Simulation results demonstrate that the proposed algorithm possess high resolution, robust to additive noise, reduction computational burden and so on.

[1]  J. Capon High-resolution frequency-wavenumber spectrum analysis , 1969 .

[2]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[3]  Bhaskar D. Rao,et al.  Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.

[4]  Geert Leus,et al.  Direction estimation using compressive sampling array processing , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

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

[6]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..

[7]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[8]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.