Non-stationary signal processing using time-frequency filter banks with applications
Signal Processing - Fractional calculus applications in signals and systems
Extraction and Analysis of Multiple Periodic Motions in Video Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separating more sources than sensors using time-frequency distributions
EURASIP Journal on Applied Signal Processing
Time-frequency signal synthesis and its application in multimedia watermark detection
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Analysis of multicomponent AM-FM signals using FB-DESA method
Digital Signal Processing
The self-duality of discrete short-time Fourier transform and its applications
IEEE Transactions on Signal Processing
Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions
Digital Signal Processing
A new blind method for separating M+1 sources from M mixtures
Computers & Mathematics with Applications
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A new approach to the analysis and reconstruction of multicomponent nonstationary signals from their time-frequency distribution (TFD) is presented. Specifically, we consider a TFD based on the recently introduced minimum cross entropy principle (MCE). This positive TFD is cross-terms free and, hence, has an advantage over the family of bilinear distributions. Based on the MCE-TFD, a new algorithm for reconstructing the phase and amplitude parameters of each component of the signal is developed. To evaluate the accuracy of the algorithm. Monte Carlo simulations are presented and compared with the corresponding Cramer-Rao bound. It is shown that the new algorithm is superior to presently available methods in both efficiency and performance. It is concluded that together with the MCE-TFD representation, the proposed approach provides a powerful tool for analysis of nonstationary multicomponent signals embedded in additive Gaussian noise