Singular Value Decomposition and Its Application to Autoregressive Parametric Spectral Estimation Singular Value Decomposition and Its Application to Autoregressive Parametric Spectral Estimation

During recent years much interest has been given to the application of Singular Value Decomposition in association with extended{order and overdetermined evaluation in the nite parametric spectral estimation domain. Such approaches have been shown to perform superior to other methods for discontinuous frequency signal, e.g. the harmonic retrieval problem. In this report a similar approach is applied to wide{banded AR processes. It is found that the so{called extraneous poles of the lower rank solution spoil the spectral estimate. A new approach, the direction weighted total least squares solution, which enforces the extraneous poles to be located at the origin while maintaining the good properties of the aforementioned approaches, is therefore introduced. Computer simulationexperiments clearly indicate that this approach is superior to existing overdetermined and extended{ or parsimonic order methods.