Eigenstructure based partially adaptive array design

A procedure is presented for designing partially adaptive arrays having nearly fully adaptive performance under steady-state conditions. Theory predicts that the required adaptive dimension is less than or equal to the rank of the spatially/temporally correlated portion of the interference correlation matrix for arbitrary linearly constrained minimum-variance beamformers. Knowledge of the eigenstructure of the interference correlation matrix is required to implement a beamformer with this adaptive dimension. To avoid adaptive estimation of the eigenstructure, the eigenstructure of an averaged correlation matrix (which spans the interference scenarios of interest) is utilized, and the adaptive dimension is given by the rank of the averaged correlation matrix. Construction of a transformation for reducing adaptive dimension is addressed. Simulations illustrating the utility of the eigenstructure approach are provided