Abstract
In the conventional robust adaptive beamformers (RABs), a common problem is that the estimated manifold vector of the signal of interest (SOI) may converge to the interference subspace. By using the data dependent constraints, such problem can be mitigated. In this paper, we propose to employ the oblique projection to obtain the SOI covariance matrix and compute the interference covariance matrix in an estimate-and-subtract manner. Rather than using the semidefinite programming relaxation, we find that the proposed RAB, which belongs to the well known non-convex quadratically constrained quadratic programming problem, can be solved efficiently by the Lagrange multiplier methodology coupled with the Newton’s method. Our simulation results demonstrate the superiority of the proposed method over other previously developed RAB techniques.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.