A HIGH-RESOLUTION TECHNIQUE FOR MULTIDIMENSIONAL NMR-SPECTROSCOPY

In this paper, a scheme for estimating frequencies and damping factors of multidimensional nuclear magnetic resonance (NMR) data is presente d, multidimensional NMR data can be modelled as the sum of several mul tidimensional damped sinusoids, The estimated frequencies and damping factors of multidimensional MIR data play important roles in determini ng protein structures, In this paper we present a high-resolution subs pace method for estimating the parameters of NMR data. Unlike other me thods, this algorithm makes full use of the rank-deficiency and Hankel properties of the prediction matrix composed of NMR data, Hence, it c an estimate the signal parameters under low signal-to-noise ratio (SNR ) by using a fem data points, The effectiveness of the new algorithm i s confirmed by computer simulations and it is tested by experimental d ata.