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Sensors (Basel). 2014 Dec 24;15(1):110-34. doi: 10.3390/s150100110.

A steady-state Kalman predictor-based filtering strategy for non-overlapping sub-band spectral estimation.

Author information

1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. lizenghui11@mails.tsinghua.edu.cn.
2
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. b-xu11@mails.tsinghua.edu.cn.
3
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. yangjian.ee@gmail.com.
4
Xi'an Research Institute of Hi-Technology, Xi'an 710025, China. Songjianshe09@126.com.

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.

PMID:
25609038
PMCID:
PMC4327010
DOI:
10.3390/s150100110
[Indexed for MEDLINE]
Free PMC Article

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