On the Characterization of Time Scale Underwater Acoustic Signals Using Matching Pursuit Decomposition

Nicolas Josso (1) , Jun J. Zhang (2) , Antonia Papandreou-Suppappola (2) , Cornel Ioana (1) , Jerome I. Mars (1) , Cedric Gervaise (3) , Yann Stéphan (4)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 SPAS Lab - Signal Processing and Adaptive Sensing (SPAS) Laboratory
3 DTN - Développement des Technologies Nouvelles
4 SHOM - Service Hydrographique et Océanographique de la Marine

We investigate a characterization of underwater acoustic signals using extracted time-scale features of the propagation channel model for medium-to-high frequency range. The underwater environment over these frequencies causes multipath and Doppler scale changes on the transmitted signal. This is the result of the time-varying nature of the channel and also due to the relative motion between the transmitter-channel-receiver configuration. As a sparse model is essential for processing applications and for practical use in simulations, we employ the matching pursuit decomposition algorithm to estimate the channel time delay and Doppler scale change model attributes for each propagating path. The proposed signal characterization was validated for sparse channel profiles using real-time data from the BASE07 experiment.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Nicolas Josso, Jun J. Zhang, Antonia Papandreou-Suppappola, Cornel Ioana, Jerome I. Mars, et al.. On the Characterization of Time Scale Underwater Acoustic Signals Using Matching Pursuit Decomposition. OCEANS 2009 - OCEANS '09 MTS/IEEE. Marine Technology for our Future: Global and Local Challenges, Oct 2009, Biloxi, United States. pp.n/c. ⟨hal-00455324⟩