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International Journal of Antennas and Propagation
Volume 2015, Article ID 959856, 10 pages
Research Article

Adaptive Beamforming Based on Compressed Sensing with Smoothed Norm

School of Electronic and Optical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China

Received 22 September 2014; Revised 12 February 2015; Accepted 18 February 2015

Academic Editor: Giuseppe Castaldi

Copyright © 2015 Yubing Han and Jian Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An adaptive beamforming based on compressed sensing with smoothed norm for large-scale sparse receiving array is proposed in this paper. Because of the spatial sparsity of the arriving signal, compressed sensing is applied to sample received signals with a sparse array and reduced channels. The signal of full array is reconstructed by using a compressed sensing reconstruction method based on smoothed norm. Then an iterative linearly constrained minimum variance beamforming algorithm is adopted to form antenna beam, whose main lobe is steered to the desired direction and nulls to the directions of interferences. Simulation results and Monte Carlo analysis for linear and planar arrays show that the beam performances of our proposed adaptive beamforming are similar to those of full array antenna.