Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Recursive updating the eigenvalue decomposition of a covariancematrix
IEEE Transactions on Signal Processing
Performance analysis of the minimum variance beamformer in thepresence of steering vector errors
IEEE Transactions on Signal Processing
Robust presteering derivative constraints for broadband antennaarrays
IEEE Transactions on Signal Processing
Doubly constrained robust Capon beamformer
IEEE Transactions on Signal Processing
A recursive least squares implementation for LCMP beamforming underquadratic constraint
IEEE Transactions on Signal Processing
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Adaptive arrays suffer from performance degradation in the presence of steering vector errors. The doubly constrained robust Capon beamformer (DCRCB) can deal with the problem, utilizing all the eigenvalues and eigenvectors of the covariance matrix, which leads to high computational complexity. This paper presents a robust beamforming method which is computationally efficient, exploiting principal eigenpairs only. The eigenpairs can be estimated based on the projection approximation subspace tracking with deflation (PASTd). The original PASTd algorithm, which does not provide orthonormal eigenvectors in general, is modified so that the orthonormalization of eigenvectors can be efficiently made using the structure of the modified algorithm. The proposed beamforming method significantly reduces the computational load, particularly when the number of the directional signals is much less than that of sensor elements, and substantially has the same performance as the conventional one utilizing all the eigenpairs.