A steady-state Kalman predictor-based filtering strategy for non-overlapping sub-band spectral estimation.
Sensors (Basel)
; 15(1): 110-34, 2014 Dec 24.
Article
em En
| MEDLINE
| ID: mdl-25609038
ABSTRACT
This paper focuses on suppressing spectral overlap for sub-band spectral estimation, with which we can greatly decrease the computational complexity of existing spectral estimation algorithms, such as nonlinear least squares spectral analysis and non-quadratic regularized sparse representation. Firstly, our study shows that the nominal ability of the high-order analysis filter to suppress spectral overlap is greatly weakened when filtering a finite-length sequence, because many meaningless zeros are used as samples in convolution operations. Next, an extrapolation-based filtering strategy is proposed to produce a series of estimates as the substitutions of the zeros and to recover the suppression ability. Meanwhile, a steady-state Kalman predictor is applied to perform a linearly-optimal extrapolation. Finally, several typical methods for spectral analysis are applied to demonstrate the effectiveness of the proposed strategy.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise Espectral
/
Algoritmos
/
Processamento de Sinais Assistido por Computador
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2014
Tipo de documento:
Article
País de afiliação:
China