Global versus local minimization in least-squares AR spectral estimation

Abstract

A general framework for power spectral estimation is presented. The equivalence among MEM, AR modeling, and predictive estimation is established. The practical determination of the AR parameters is reviewed. A new classification method for the least-squares AR spectral estimation methods is introduced: the global and local minimization methods. The bordering algorithm for the solution of a system of linear equations is used to deduce Levinson algorithm and to show that Burg Method and a Modified Burg Method are local methods and approximations to a global minimization method. A brief comparative discussion of global versus local least-squares estimation is done.