Structured least squares criterion for linear prediction

Linear prediction is one of the most important tools in modern signal processing. This article is concerned with the optimization of the linear prediction coefficients through a least squares criterion taking into account the special structures of the associated data matrix. These structures are lost during the conventional least squares optimization. However better results can be achieved if they are preserved. We propose two procedures to this end and demonstrate that they are equivalent because they minimize the same objective function