Adaptive beamforming using recursive eigenstructure updating with subspace constraint
Kai‐Bor Yu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
An algorithm is presented for updating the adaptive beamformer weights using recursive eigenvalue decomposition (EVD) of a covariance matrix and subspace constraint. This algorithm exploits the subspace structure that the covariance matrix of the interference sources and the noise is a low-rank matrix plus a diagonal matrix. This eigenspace characterization approach avoids the numerically unstable recursive procedure based on the matrix inversion lemma. Moreover, the subspace property makes it possible to develop a fast algorithm by monitoring only the principal eigenvalues and eigenvectors and the noise eigenvalue.