Most reliable null space vector and its application to direction finding
Paul D. Fiore · 2003
Given a data matrix, we desire a null space vector that is the most robust against correlated Gaussian noise perturbations. A closed-form calculation of the most reliable null space vector is presented. An attempt to modify the min-norm direction finding algorithm for use in spatio-temporal colored noise is investigated.