Journal of magnetic resonance series a vol:119 issue:2 pages:225-234
Many parameter-estimation algorithms have been developed for the accurate quantification of NMR data modeled as a sum of K exponentially damped sinusoids. Some well-known time-domain techniques based on subspace estimation and the singular value decomposition are Kumaresan and Tuft's linear prediction method and Kung et al.'s method based on state-space modeling, etc. All these methods do not use prior knowledge, except 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 methods to demonstrate the advantage of the new method. (C) 1996 Academic Press, Inc.