2014/01/20 by P. Li, Li, P., Rodrigo C. de Lamare +2
Computer Science · Engineering · Mathematics · #Advanced Adaptive Filtering Techniques #Direction-of-Arrival Estimation Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Speech and Audio Processing #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1401.4936
5 pages, 3 figures. CAMSAP 2013
arxiv created 2014/01/20 · openalex publication_date 2014/01/20 · arxiv updated 2014/01/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.