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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
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Author to whom correspondence should be addressed.
Sensors 2015, 15(1), 110-134; https://doi.org/10.3390/s150100110
Received: 20 October 2014 / Accepted: 17 December 2014 / Published: 24 December 2014
(This article belongs to the Section Physical Sensors)
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
MDPI and ACS Style

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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