A high-resolution technique for multidimensional NMR spectroscopy.

Abstract

In this paper, a scheme for estimating frequencies and damping factors of multidimensional nuclear magnetic resonance (NMR) data is presented, multidimensional NMR data can be modeled as the sum of several multidimensional damped sinusoids. The estimated frequencies and damping factors of multidimensional NMR data play important roles in determining protein structures. In this paper we present a high-resolution subspace method for estimating the parameters of NMR data. Unlike other methods, this algorithm makes full use of the rank-deficiency and Hankel properties of the prediction matrix composed of NMR data. Hence, it can estimate the signal parameters under low signal-to-noise ratio (SNR) by using a few data points. The effectiveness of the new algorithm is confirmed by computer simulations and it is tested by experimental data.