This paper presents a new state-space method for spectral estimation that performs decimation by any factor, it makes use of the full\r\nset of data and brings further apart the poles under consideration, while imposing almost no constraints to the size of the Hankel\r\nmatrix (model order), as decimation increases. It is compared against two previously proposed techniques for spectral estimation\r\n(along with derived decimative versions), that lie among the most promising methods in the field of spectroscopy, where accuracy\r\nof parameter estimation is of utmost importance. Moreover, it is compared against a state-of-the-art purely decimative method\r\nproposed in literature. Experiments performed on simulated NMR signals prove the new method to be more robust, especially for\r\nlow signal-to-noise ratio.
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