There are also model based methods that attempt to force or exploit the structure of the covariance interference matrix. The more generally applicable of these methods is the covariance taper matrix structure. The goal is to compactly model the interference, at which point it can then be processed using principal component techniques or diagonal-loading SMI (where a small magnitude, random diagonal matrix is added to attempt to stabilize the matrix prior to inverting). This modeling has an added benefit of decorrelating interference subspace leakage (ISL), and is resistant to internal clutter motion (ICM). The principal component method firsts applies principal component analysis to estimate the dominant eigenvalues and eigenvectors, and then applies a covariance taper and adds an estimated noise floor: