PARAMETER-ESTIMATION WITH PRIOR KNOWLEDGE OF KNOWN SIGNAL POLES FOR THE QUANTIFICATION OF NMR-SPECTROSCOPY DATA IN THE TIME-DOMAIN

Many parameter-estimation algorithms have been developed for the accur ate quantification of NMR data modeled as a sum of K exponentially dam ped sinusoids. Some well-known time-domain techniques based on subspac e estimation and the singular value decomposition are Kumaresan and Tu ft's linear prediction method and Kung et al.'s method based on state- space modeling, etc. All these methods do not use prior knowledge, exc ept the formulation of the data model and the model order estimate K. The best accuracy is obtained with a variant of Kung's method, called HTLS, using the total least-squares principle. In this paper, the HTLS method is extended to the HTLS-PK method, which has the capability to accommodate prior knowledge of some known signal poles. Simulated and real-world NMR signals are processed using the HTLS and HTLS-PK metho ds to demonstrate the advantage of the new method. (C) 1996 Academic P ress, Inc.