  This paper presents a low-complexity robust data-dependent dimensionality
reduction based on a modified joint iterative optimization (MJIO) algorithm for
reduced-rank beamforming and steering vector estimation. The proposed robust
optimization procedure jointly adjusts the parameters of a rank-reduction
matrix and an adaptive beamformer. The optimized rank-reduction matrix projects
the received signal vector onto a subspace with lower dimension. The
beamformer/steering vector optimization is then performed in a
reduced-dimension subspace. We devise efficient stochastic gradient and
recursive least-squares algorithms for implementing the proposed robust MJIO
design. The proposed robust MJIO beamforming algorithms result in a faster
convergence speed and an improved performance. Simulation results show that the
proposed MJIO algorithms outperform some existing full-rank and reduced-rank
algorithms with a comparable complexity.
