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Sensors 2015, 15(1), 110-134; doi:10.3390/s150100110

A Steady-State Kalman Predictor-Based Filtering Strategy for Non-Overlapping Sub-Band Spectral Estimation

1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
Xi'an Research Institute of Hi-Technology, Xi'an 710025, China
*
Author to whom correspondence should be addressed.
Received: 20 October 2014 / Accepted: 17 December 2014 / Published: 24 December 2014
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: AR model; equiripple FIR filter; linear prediction; spectral estimation; spectral overlap; sub-band decomposition AR model; equiripple FIR filter; linear prediction; spectral estimation; spectral overlap; sub-band decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, Z.; Xu, B.; Yang, J.; Song, J. A Steady-State Kalman Predictor-Based Filtering Strategy for Non-Overlapping Sub-Band Spectral Estimation. Sensors 2015, 15, 110-134.

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