Optimal kernels for time-frequency analysis

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Abstract
Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However the choice of a fixed kernel limits the class ofsignals that can be analyzed effectively. This paper presents optimality criteria for the design of signal-dependeni kernels that suppress cross-components while passing as much auto-component energy as possible irrespective of the form of the signal. A fast algorithm for the optimal kernel solution makes the procedure competitive computationaily with fixed kernel methods. Examples demonstrate the superior performance of the optimal kernel for a frequency modulated signal.

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