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The design matrix, the normal equations, the pseudoinverse, and the hat matrix (projection matrix).
An efficient method is derived for the optimal projection matrix design with a given Gram matrix.
The optimal projection matrix is obtained by mainly minimizing the average coherence of the equivalent dictionary.
Secondly, a restraint item is adopted to constrain the back projection matrix.
The main novelty is the construction of the parameter-dependent projection matrix.
They are calculated based on the error projection matrix in the back projection process.
We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter.
The technique is derived from the Petrov Galerkin projection by adding constraints on the projection matrix.
The projection matrix design problem is formulated as an optimization problem, which minimizes the mean squared error of the estimated sparse signal with the energy of the projection matrix being constrained.
Moreover, by the relation between signal and noise subspace, the corresponding projection matrix of noise subspace is obtained, hence an improved MUSIC algorithm is implemented by this projection matrix.
We present a novel robust parametric model order reduction method using matrix interpolation and common projection matrix.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com