Title: Constrained Least Squares for Extended Complex Factor Analysis,
Abstract: For subspace estimation with an unknown colored noise, Factor Analysis (FA)
is a good candidate for replacing the popular eigenvalue decomposition (EVD).
Finding the unknowns in factor analysis can be done by solving a non-linear
least square problem. For this type of optimization problems, the Gauss-Newton
(GN) algorithm is a powerful and simple method. The most expensive part of the
GN algorithm is finding the direction of descent by solving a system of
equations at each iteration. In this paper we show that for FA, the matrices
involved in solving these systems of equations can be diagonalized in a closed
form fashion and the solution can be found in a computationally efficient way.
We show how the unknown parameters can be updated without actually constructing
these matrices. The convergence performance of the algorithm is studied via
numerical simulations.