Estimation of Instantaneous Frequency and Amplitude of Multi-Component Signals using Sparse Modeling of Signal Innovation
Résumé
This paper introduces a new method for estimating modes in non-stationary mixture signals. First, we establish a connection between the short-time Fourier transform (STFT) and sparse sampling theory, representing observations as pulses filtered by a known function. Leveraging the finite rate of innovation in the target signal, our specialized reconstruction approach enables mode estimation amidst noise.
Second, we propose a variant based on a recursive version of the STFT allowing real-time mode parameter estimation with sequential acquisition.
We compare our results with state-of-the-art methods, showing an improvement in estimation performance across various scenarios.
Our approach paves the way of the future mode disentangling algorithms based on Finite rate of innovation.
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