title: A steady-state Kalman predictor-based filtering strategy for non-overlapping sub-band spectral estimation.
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.
authors.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. lizenghui11@mails.tsinghua.edu.cn.
authors.forename: Zenghui
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. b-xu11@mails.tsinghua.edu.cn.
authors.forename: Bin
authors.initials: B
authors.lastname: Xu
authors.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. yangjian.ee@gmail.com.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.affiliation: Xi'an Research Institute of Hi-Technology, Xi'an 710025, China. Songjianshe09@126.com.
authors.forename: Jianshe
authors.initials: J
authors.lastname: Song
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D012815:Signal Processing, Computer-Assisted; D013057:Spectrum Analysis