Title :
Low-complexity implementation for worst-case optimization-based robust adaptive beamforming
Author_Institution :
Underwater Acoust. Syst. Lab., Hangzhou Appl. Acoust. Res. Inst., Hangzhou
Abstract :
In this paper, an efficient low-complexity robust adaptive beamforming method based on worst-case performance optimization is proposed. Lagrangian method was applied to obtain the expression for the robust adaptive weight vector, which is optimized on the boundary of the steering vector uncertainty region, that is to say, in the worst mismatch case. Combining the constraint condition and the eigendecomposition of the array covariance matrix, root-finding method is used to obtain the optimal Lagrange multiplier. Then, the diagonal loading-like robust weight vector is achieved. The implementation efficiency is greatly improved since the main computational burden is the eigendecomposition operator. Numerical results show that the performance of the proposed method is nearly identical to the robust Capon beamforming.
Keywords :
array signal processing; covariance matrices; eigenvalues and eigenfunctions; Lagrangian method; array covariance matrix; diagonal loading-like robust weight vector; eigendecomposition; robust Capon beamforming; robust adaptive beamforming; robust adaptive weight vector; root-finding method; steering vector; worst-case optimization; Array signal processing; Covariance matrix; Interference; Lagrangian functions; Optimization methods; Robustness; Sensor arrays; Signal to noise ratio; Uncertainty; Underwater acoustics;
Conference_Titel :
Sensor Array and Multichannel Signal Processing Workshop, 2008. SAM 2008. 5th IEEE
Conference_Location :
Darmstadt
Print_ISBN :
978-1-4244-2240-1
Electronic_ISBN :
978-1-4244-2241-8
DOI :
10.1109/SAM.2008.4606879