Abstract
Many adaptive beamforming algorithms deteriorate drastically in practical applications because they are highly sensitive to mismatches between exploited assumptions and the actual characteristics of the sensor array, environment and sources. Recently, the Robust Capon Beamformer (RCB) is derived which utilizes the uncertainty set of the desired array steering vector and is capable of handling arbitrary array steering vector mismatches. However, when large steering vector mismatches occur, the uncertainty set has to be expanded to accommodate for the increased uncertainty and this degrades the output signal-to-interference-plus-noise ratio (SINR) of the RCB. In this paper, an iterative RCB is proposed which makes use of a small uncertainty sphere to search for the desired array steering vector. This proposed method provides higher SINR than the conventional RCB by avoiding the use of a large uncertainty search sphere. Unlike the conventional RCB, this iterative RCB does not require knowledge of the uncertainty level in the desired array steering vector. Theoretical analysis and computer simulations illustrate the effectiveness of the proposed beamformer.
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.
