Visible to the public Biblio

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2020-12-21
Han, K., Zhang, W., Liu, C..  2020.  Numerical Study of Acoustic Propagation Characteristics in the Multi-scale Seafloor Random Media. 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP). :135–138.
There is some uncertainty as to the applicability or accuracy of current theories for wave propagation in sediments. Numerical modelling of acoustic data has long been recognized to be a powerful method of understanding of complicated wave propagation and interaction. In this paper, we used the coupled two-dimensional PSM-BEM program to simulate the process of acoustic wave propagation in the seafloor with distributed multi-scale random media. The effects of fluid flow between the pores and the grains with multi-scale distribution were considered. The results show that the coupled PSM-BEM program can be directly applied to both high and low frequency seafloor acoustics. A given porous frame with the pore space saturated with fluid can greatly increase the magnitude of acoustic anisotropy. acoustic wave velocity dispersion and attenuation are significant over a frequency range which spans at least two orders of magnitude.
2015-05-04
Shakeri, S., Leus, G..  2014.  Underwater ultra-wideband fingerprinting-based sparse localization. Signal Processing Advances in Wireless Communications (SPAWC), 2014 IEEE 15th International Workshop on. :140-144.

In this work, a new fingerprinting-based localization algorithm is proposed for an underwater medium by utilizing ultra-wideband (UWB) signals. In many conventional underwater systems, localization is accomplished by utilizing acoustic waves. On the other hand, electromagnetic waves haven't been employed for underwater localization due to the high attenuation of the signal in water. However, it is possible to use UWB signals for short-range underwater localization. In this work, the feasibility of performing localization for an underwater medium is illustrated by utilizing a fingerprinting-based localization approach. By employing the concept of compressive sampling, we propose a sparsity-based localization method for which we define a system model exploiting the spatial sparsity.