Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Joint time-frequency analysis: methods and applications
Joint time-frequency analysis: methods and applications
Time-frequency representation based on the reassigned S-method
Signal Processing
Efficient analysis of time-varying multicomponent signals with modified LPTFT
EURASIP Journal on Applied Signal Processing
The reassigned local polynomial periodogram and its properties
Signal Processing
Split-Radix Algorithms for Arbitrary Order of Polynomial Time Frequency Transforms
IEEE Transactions on Signal Processing
On the computation of two-dimensional DCT
IEEE Transactions on Signal Processing
Discrete chirp-Fourier transform and its application to chirp rateestimation
IEEE Transactions on Signal Processing
Improving the readability of time-frequency and time-scalerepresentations by the reassignment method
IEEE Transactions on Signal Processing
Generalized Fast Algorithms for the Polynomial Time-Frequency Transform
IEEE Transactions on Signal Processing
New polynomial transform algorithm for multidimensional DCT
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Fast DHT algorithms for length N=q*2m
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
In this paper, the reassignment method is extended to the robust local polynomial periodogram to process the nonstationary signals corrupted by impulse noise. Performance comparisons between robust spectrogram, the reassigned robust spectrogram, the robust local polynomial periodogram and the reassigned robust local polynomial periodogram (RrLPP) are presented. During computation of the local polynomial Fourier transforms, the length of overlap between the adjacent data segments is reduced to minimize the computational complexity. It shows that under the environment of impulse noise, with reduced overlap, the RrLPP and the reassigned robust local polynomial periodogram along frequency direction (RfrLPP) achieve much better signal concentrations than their counterparts without reassignment. It is also observed that the RfrLPP can achieve comparable results as the RrLPP, with reduced computational time.