Robust Reduced-Rank Adaptive LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Parameters - Advances in Heuristic Signal Processing and Applications - page 32

Fig. 2.6 SINR performance of RJIO-LCMV ( a )SGand( b ) RLS algorithms against snapshots

with M = 24, SNR = 12 dB with rank adaptation

15 ◦ and 30 ◦ , whereas one interferer with DoA 45 ◦
and a power level equal to the SoI exits the system. The RJIO and other analyzed
adaptive beamforming algorithms are equipped with rank adaptation techniques and
have to adjust their parameters in order to suppress the interferers. We optimize the
step sizes and the forgetting factors of all the algorithms in order to ensure that
they converge as fast as they can to the same value of SINR. The results of this
experiment are depicted in Fig. 2.7 . The curves show that the RJIO algorithms have
a superior performance to the existing algorithms considered in this study.

45 ◦ , the system with DoAs − −

2.7 Conclusions

We have investigated robust reduced-rank LCMV beamforming algorithms based on
robust joint iterative optimization of beamformers. The RJIO reduced-rank scheme
is based on a robust constrained joint iterative optimization of beamformers accord-
ing to the minimum variance criterion. We derived robust LCMV expressions for
the design of the rank-reduction matrix and the reduced-rank beamformer and de-
veloped SG and RLS adaptive algorithms for their efficient implementation along
with a rank adaptation technique. The numerical results for an adaptive beamform-
ing application with a ULA have shown that the RJIO scheme and algorithms out-
perform in convergence, steady state and tracking the existing robust full-rank and
reduced-rank algorithms at comparable complexity. The proposed algorithms can
be extended to other array geometries and applications.