Paper Info
Title
Adaptive Uncertainty Based Iterative Robust Capon Beamformer Using Steering Vector Mismatch Estimation
Abstract
To overcome the signal-to-interference-and-noise ratio (SINR) performance degradation in the presence of large steering vector mismatches, we propose an iterative robust Capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector (SV) based on conventional robust Capon beamformer (RCB) formulation that uses an uncertainty sphere to model the mismatch between the actual and presumed SV. At each iteration, the adaptive uncertainty algorithm self-adjusts the uncertainty sphere according to the estimated mismatch SV. This estimation is derived based on the geometrical interpretation of the mismatch and can be expressed as a simple closed-form expression as a function of the presumed SV and the signal-subspace projection. The other variant of the proposed algorithm that uses a flat ellipsoid to model the mismatch is also proposed. Simulation results show that the proposed approaches offer better interference suppression capability and achieve higher output SINR, as compared to other diagonal-loading-based approaches.
Year
DOI
Venue
2011
10.1109/TSP.2011.2157500
IEEE Transactions on Signal Processing
Keywords
DocType
Volume
adaptive signal processing,array signal processing,interference suppression,iterative methods,adaptive uncertainty algorithm,array signal processing,closed-form expression,interference suppression capability,iterative robust Capon beamformer,signal-subspace projection,signal-to-interference-and-noise ratio performance degradation,steering vector mismatch estimation,uncertainty sphere,Array signal processing,adaptive arrays,iterative methods,parameter estimation,robustness
Journal
59
Issue
ISSN
Citations 
9
1053-587X
16
PageRank 
References 
Authors
0.92
19
3
Name
Order
Citations
PageRank
Joni Polili Lie1637.43
Wee Ser262967.09
Chong Meng Samson See317519.41