Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3390/s150100110 | SENSORS |
Keywords | Field | DocType |
AR model,equiripple FIR filter,linear prediction,spectral estimation,spectral overlap,sub-band decomposition | Autoregressive model,Spectral density estimation,Mathematical optimization,Maximum entropy spectral estimation,Filter (signal processing),Algorithm,Kalman filter,Linear prediction,Electronic engineering,Extrapolation,Non-linear least squares,Engineering | Journal |
Volume | Issue | ISSN |
15 | 1.0 | 1424-8220 |
Citations | PageRank | References |
1 | 0.36 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zenghui Li | 1 | 1 | 0.70 |
| Bin Xu | 2 | 133 | 23.23 |
| Jian Yang | 3 | 483 | 64.80 |
| Jianshe Song | 4 | 1 | 0.36 |