Low-complexity robust data-dependent dimensionality reduction based on joint iterative optimization of parameters

Abstract

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. * This work was supported by UK MOD under contract from the Centre for Defence Enterprise Capon Beamforming (RCB) technique of [2]. A key limitation of [2] and other robust techniques [3,4,5] is their high cost for large sensor arrays and their suitability to dynamic environments.

Robust Adaptive Beamforming Based on Low-Rank and Cross-Correlation Techniques

“…The work in [27] has developed a recursive least squares (RLS) adaptive algorithm based on widelylinear processing using the JIO technique. The study in [29] has devised efficient stochastic gradient (SG) and RLS RAB algorithms from a modified JIO (MJIO) scheme.…”