Abstract
In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent dimensionality reduction based on an iterative optimization with steering vector perturbation (IOVP) 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 IOVP design. The proposed robust IOVP beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed IOVP algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity.
Highlights
Adaptive beamforming algorithms often encounter problems when they operate in dynamic environments with large sensor arrays
Steering vector mismatches are often caused by calibration/pointing errors, and a high complexity is usually introduced by an expensive inverse operation of the covariance matrix of the received data
The candidate steering vectors are responsible for performing rank reduction, and the reduced-rank beamformer forms the beam in the direction of the signal of interest (SoI)
Summary
Adaptive beamforming algorithms often encounter problems when they operate in dynamic environments with large sensor arrays These problems include steering vector mismatches, high computational complexity and snapshot deficiency. The advantage of reduced-rank methods lies in their superior convergence and tracking performance achieved by exploiting the low-rank nature of the signals It offers a large reduction in the required number of training samples over full-rank methods [2], which may addresses the problem of snapshot deficiency at low complexity. In order to address this problem, in this paper, we introduce a low-complexity robust data-dependent dimensionality reduction algorithm for reduced-rank beamforming and steering vector estimation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
