Analysis of underwater signals with nonlinear time-frequency structures using warping based compressive sensing algorithm
Résumé
Natural signals are often characterized by nonlinear time-frequency structures and more especially in underwater context. Underwater mammal vocalizations or dispersive phenomena are just some examples of contexts where nonlinear time-frequency structures of signal's components exist. Their analysis is of great importance for detection and classification purposes but also for phenomenon characterization.In this work, starting from the concept of warping-based time-frequency analysis, we propose a new analysis method that combines the properties of the waping transform with the concept of compressive sensing. It provides a more accurate characterization of nonlinear time-frequency structures in terms of the estimation of their parameters. Results provided for simulated data prove the interst of this new approach with respect to the spectrogram-based method.