Title :
A batch least squares lattice algorithm
Author_Institution :
Integrated Syst. Inc., Santa Clara, CA, USA
Abstract :
A fast square root batch least squares algorithm for autoregressive model structures that requires only seven floating point operations per sample per estimated parameter is derived. Memory requirements, as well as the number of floating point operations, are of order n, where n is the model order. The method is based on estimation of the top block row of the QR transform of the data regression matrix. This is used to derive the parameters using an order-recursive lattice algorithm, after all samples have been processed
Keywords :
least squares approximations; matrix algebra; statistical analysis; time series; QR transform; autoregressive model structures; batch least squares lattice algorithm; data regression matrix; fast square root batch least squares algorithm; floating point operations; memory requirements; model order; order-recursive lattice algorithm; Equations; Lattices; Least squares approximation; Least squares methods; Parameter estimation; Stacking; Symmetric matrices; Transforms; Writing;
Conference_Titel :
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-0872-7
DOI :
10.1109/CDC.1992.371195
