An L1-constrained normalized lms algorithm and its application to thinned adaptive antenna arrays

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 3806 - 3810
• Main Authors: de Andrade, J. F., de Campos, M. L. R., Apolinario, J. A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2013
• Subjects: Abstracts; Adaptive arrays; CNLMS Algorithm; Constrained Adaptive Beamforming; Indexes; Jamming; L 1 -norm; Sparse Sensor Arrays; Thinned Arrays
• ISSN: 1520-6149
• DOI: 10.1109/ICASSP.2013.6638370

We propose in this work an L 1 -norm Linearly Constrained Normalized Least-Mean-Square (L 1 -CNLMS) algorithm applied to solve the beamforming problem in Standard Hexagonal Arrays (SHA) and (non-standard) Hexagonal Antenna Arrays (HAA). In addition to the linear constraints present in the CNLMS algorithm, the L 1 -CNLMS algorithm takes into account an L 1 -norm penalty on the filter coefficients which results in sparse solutions producing Thinned Hexagonal Arrays. The effectiveness of the L 1 -CNLMS algorithm is demonstrated by comparing, via computer simulations, its results with those of the CNLMS algorithm. When employing the L 1 -CNLMS algorithm to antenna array problems, the resulting effect of the L 1 -norm constraint is perceived as a large aperture array with few active array elements.