Journal of Applied Mathematics
- J. Appl. Math.
- Volume 2013, Special Issue (2013), Article ID 245609, 8 pages.
Doubly Constrained Robust Blind Beamforming Algorithm
We propose doubly constrained robust least-squares constant modulus algorithm (LSCMA) to solve the problem of signal steering vector mismatches via the Bayesian method and worst-case performance optimization, which is based on the mismatches between the actual and presumed steering vectors. The weight vector is iteratively updated with penalty for the worst-case signal steering vector by the partial Taylor-series expansion and Lagrange multiplier method, in which the Lagrange multipliers can be optimally derived and incorporated at each step. A theoretical analysis for our proposed algorithm in terms of complexity cost, convergence performance, and SINR performance is presented in this paper. In contrast to the linearly constrained LSCMA, the proposed algorithm provides better robustness against the signal steering vector mismatches, yields higher signal captive performance, improves greater array output SINR, and has a lower computational cost. The simulation results confirm the superiority of the proposed algorithm on beampattern control and output SINR enhancement.
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 245609, 8 pages.
First available in Project Euclid: 7 May 2014
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Song, Xin; Ren, Jingguo; Li, Qiuming. Doubly Constrained Robust Blind Beamforming Algorithm. J. Appl. Math. 2013, Special Issue (2013), Article ID 245609, 8 pages. doi:10.1155/2013/245609. https://projecteuclid.org/euclid.jam/1399493691