A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
RELAX-based estimation of damped sinusoidal signal parameters
Signal Processing
Fast Implementation of Two-Dimensional APES and CAPON Spectral Estimators
Multidimensional Systems and Signal Processing
Estimating local multiple orientations
Signal Processing
Time-frequency analysis using warped-based high-order phase modeling
EURASIP Journal on Applied Signal Processing
Recursive and fast recursive capon spectral estimators
EURASIP Journal on Applied Signal Processing
Analysis of multiple orientations
IEEE Transactions on Image Processing
Efficient algorithms for adaptive capon and APES spectral estimation
IEEE Transactions on Signal Processing
An adaptive filtering approach to spectral estimation and SARimaging
IEEE Transactions on Signal Processing
Improving the readability of time-frequency and time-scalerepresentations by the reassignment method
IEEE Transactions on Signal Processing
Efficient mixed-spectrum estimation with applications to targetfeature extraction
IEEE Transactions on Signal Processing
Time-scale energy distributions: a general class extending wavelettransforms
IEEE Transactions on Signal Processing
Product high-order ambiguity function for multicomponentpolynomial-phase signal modeling
IEEE Transactions on Signal Processing
Decomposition of the Wigner-Ville distribution and time-frequencydistribution series
IEEE Transactions on Signal Processing
An adaptive optimal-kernel time-frequency representation
IEEE Transactions on Signal Processing
Full reinforcement operators in aggregation techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IAA Spectral Estimation: Fast Implementation Using the Gohberg–Semencul Factorization
IEEE Transactions on Signal Processing
Efficient Implementation of Iterative Adaptive Approach Spectral Estimation Techniques
IEEE Transactions on Signal Processing
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When dealing with time-frequency analysis, each distribution belonging to the Cohen class (CC) is defined by its kernel. It introduces a time-frequency smoothing which impacts on the time-frequency resolution and the cross-term disappearance. Depending on the application, the practitioner has to find the ''best'' compromise. In this paper, we suggest combining several CC time-frequency representations (TFRs), corresponding to coarse-to-fine scales of smoothing. Taking advantage of this diversity, our approach consists in differentiating the signal, assumed to be characterized by 2-D near-linear stable trajectories in the time-frequency plane, and the cross-terms, assumed to be geometrically unstructured. For this purpose, a ''confidence map'' for each TFR is deduced from a local variational analysis of the time-frequency distribution. The set of confidence maps is then used to combine the different TFRs in order to obtain an ''enhanced'' TFR. Our approach is compared with various conventional TFRs by using synthetic data. Despite a comparatively higher computational cost, the resulting enhanced TFR exhibits a high time-frequency resolution while having limited cross-terms. As simulation results confirm the effectiveness of our method, it is then applied in the field of inverse synthetic aperture radar (ISAR) processing.