A New Data Extrapolation Approach Based on Spectral Partitioning
| Autor: | Giuseppe Fabrizio, Mike D. E. Turley, Van Khanh Nguyen |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
020301 aerospace & aeronautics
Mathematical optimization Least-squares spectral analysis Non-uniform discrete Fourier transform Applied Mathematics Extrapolation Spectral density estimation 020206 networking & telecommunications 02 engineering and technology Discrete Fourier transform 0203 mechanical engineering Discrete sine transform Autoregressive model Discrete Fourier series Signal Processing 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Algorithm Mathematics |
| Zdroj: | IEEE Signal Processing Letters. 23:454-458 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2016.2533602 |
| Popis: | In this letter, we propose a new linear predictive (LP) data extrapolation approach. It involves partitioning the spectrum into multiple spectral subbands and using a different autoregressive (AR) process to model each subband. The new extrapolation approach is then combined with the classical discrete Fourier transform (DFT) to produce a new hybrid LP-DFT spectral estimator to address the detection and estimation problem of multiple sinusoids in a discrete data sequence. Simulation results demonstrate the superiority of the proposed hybrid technique over an existing popular hybrid LP-DFT technique, where a single AR process is used to model the entire spectrum of the data sequence. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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