Adaptive beamformer based on the augmented complex least mean square algorithm

ABSTRACT In this article, an adaptive beamforming system based on the augmented complex least mean square algorithm is analysed. In this approach, the adaptive filter is used as a widely linear system. The second-order statistical information of the signals involved in the array is exploited. Under this consideration, the ability of the adaptive array to minimize the effects of interferences and complex white noise could be enhanced. The equations for the optimal weights and the array factor are derived for the proposed beamforming system. Computer simulations have been performed to evaluate the performance of the adaptive array, and the results were compared with two of the most common standard adaptive beamforming algorithms: the least mean square and recursive least square. The numerical simulations show that the proposed adaptive array has a better performance in time and spatial domain as compared to the classical beamforming systems.

[1]  Paulo S. R. Diniz,et al.  Adaptive Filtering: Algorithms and Practical Implementation , 1997 .

[2]  Danilo P. Mandic,et al.  Widely Linear Modeling for Frequency Estimation in Unbalanced Three-Phase Power Systems , 2013, IEEE Transactions on Instrumentation and Measurement.

[3]  Danilo P. Mandic,et al.  THE AUGMENTED COMPLEX LEAST MEAN SQUARE ALGORITHM WITH APPLICATION TO ADAPTIVE PREDICTION PROBLEMS 1 , 2008 .

[4]  Q. Feng,et al.  A robust adaptive beamforming method based on iterative optimization algorithm , 2014 .

[5]  Zhang JianYun,et al.  A Widely-Linear LMS Algorithm for Adaptive Beamformer , 2007, 2007 International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications.

[6]  Lutz H.-J. Lampe,et al.  A widely linear LMS algorithm for MAI suppression for DS-CDMA , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[7]  Bernard C. Picinbono,et al.  On circularity , 1994, IEEE Trans. Signal Process..

[8]  Danilo P. Mandic,et al.  Performance analysis of the conventional complex LMS and augmented complex LMS algorithms , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Louis L. Scharf,et al.  Second-order analysis of improper complex random vectors and processes , 2003, IEEE Trans. Signal Process..

[10]  Pascal Bondon,et al.  Second-order statistics of complex signals , 1997, IEEE Trans. Signal Process..

[12]  B. Widrow,et al.  The complex LMS algorithm , 1975, Proceedings of the IEEE.

[13]  Randy L. Haupt,et al.  Introduction to Adaptive Arrays , 1980 .

[14]  Danilo P. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters , 2009 .

[15]  R.C. Johnson,et al.  Introduction to adaptive arrays , 1982, Proceedings of the IEEE.

[16]  D. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models , 2009 .

[17]  Ali H. Sayed,et al.  Adaptive Filters , 2008 .