Abstract
In practical applications, the performance of the linearly constrained constant modulus algorithm (CMA) is known to degrade severely in the presence of even slight signal steering vector mismatches. To account for the mismatches, a novel robust CMA algorithm based on double constraints is proposed via the oblique projection of signal steering vector and the norm constraint of weight vector. To improve robustness, the weight vector is optimized to involve minimization of a constant modulus algorithm objective function by the Lagrange multipliers method, in which the parameters can be precisely derived at each iterative step. The proposed robust constrained CMA has a faster convergence rate, provides better robustness against the signal steering vector mismatches and yields improved array output performance as compared with the conventional constrained CMA. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement.
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© 2012 Springer-Verlag Berlin Heidelberg
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Song, X., Wang, J., Li, Q., Wang, H. (2012). Robust Constrained Constant Modulus Algorithm. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_43
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DOI: https://doi.org/10.1007/978-3-642-31362-2_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
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