Decimative spectral estimation with unconstrained model order. (English) Zbl 1236.62119
Summary: This paper presents a new state-space method for spectral estimation that performs decimation by any factor. It makes use of the full set of data and brings further apart the poles under consideration, while imposing almost no constraints to the size of the Hankel matrix (model order), as the decimation increases. It is compared against two previously proposed techniques for spectral estimation (along with derived decimative versions), that lie among the most promising methods in the field of spectroscopy, where accuracy of parameter estimation is of utmost importance. Moreover, it is compared against a state-of-the-art purely decimative method proposed in the literature. Experiments performed on simulated NMR signals prove that the new method is more robust, especially for low signal-to-noise ratio.
MSC:
| 62M15 | Inference from stochastic processes and spectral analysis |
| 94A12 | Signal theory (characterization, reconstruction, filtering, etc.) |
Software:
VanHuffelReferences:
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