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In this paper, a novel block fast array recursive least squares (FARLS) algorithm is developed that exploits the block Toeplitz structure of a data matrix by using its block rows as block shifted regressors for efficiently solving a regularized system of equations. A new block hyperbolic Householder transformation is used for the hyperbolic rotations. This new BFARLS algorithm is applied to the maximum likelihood (ML) estimation of ground reflectivity from linear flight strip-map synthetic aperture radar (SAR) data, whose solution involves solving a very large system of equations that has the stated structure. Simulation examples are given that illustrate the algorithm and the complexity of the algorithm is analyzed.
Date of Conference: 7-10 Nov. 2010