Joint time-frequency analysis: methods and applications
Joint time-frequency analysis: methods and applications
Local polynomial periodograms for signals with the time-varying frequency and amplitude
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
Robust L-estimation based forms of signal transforms and time-frequency representations
IEEE Transactions on Signal Processing
Improved instantaneous frequency estimation using an adaptiveshort-time Fourier transform
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Adaptive local polynomial fourier transform in ISAR
EURASIP Journal on Applied Signal Processing
The reassigned local polynomial periodogram and its properties
Signal Processing
Time--frequency feature representation using energy concentration: An overview of recent advances
Digital Signal Processing
Reassignment methods for robust time-frequency representations
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Lv's distribution for time-frequency analysis
CSCS '11 Proceedings of the 2nd international conference on Circuits, systems, control, signals
Performance analysis on Lv distribution and its applications
Digital Signal Processing
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This paper presents efficient algorithms for the analysis of nonstationary multicomponent signals based on modified local polynomial time-frequency transform. The signals to be analyzed are divided into a number of segments and the desired parameters for computing the modified local polynomial time-frequency transform in each segment are estimated from polynomial Fourier transform in the frequency domain. Compared to other reported algorithms, the length of overlap between consecutive segments is reduced to minimize the overall computational complexity. The concept of adaptive window lengths is also employed to achieve a better time-frequency resolution for each component. Numerical simulations with synthesized multicomponent signals show that the proposed ones achieve better performance on instantaneous frequency estimation with greatly reduced computational complexity.