Sensors 2013, 13(5), 6183-6203; doi:10.3390/s130506183
A Stratified Acoustic Model Accounting for Phase Shifts for Underwater Acoustic Networks
1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong
*
Author to whom correspondence should be addressed.
Received: 6 March 2013 / Revised: 22 April 2013 / Accepted: 24 April 2013 / Published: 13 May 2013
(This article belongs to the Special Issue Last Trends in Acoustic Sensing)
Abstract
Accurate acoustic channel models are critical for the study of underwater acoustic networks. Existing models include physics-based models and empirical approximation models. The former enjoy good accuracy, but incur heavy computational load, rendering them impractical in large networks. On the other hand, the latter are computationally inexpensive but inaccurate since they do not account for the complex effects of boundary reflection losses, the multi-path phenomenon and ray bending in the stratified ocean medium. In this paper, we propose a Stratified Acoustic Model (SAM) based on frequency-independent geometrical ray tracing, accounting for each ray’s phase shift during the propagation. It is a feasible channel model for large scale underwater acoustic network simulation, allowing us to predict the transmission loss with much lower computational complexity than the traditional physics-based models. The accuracy of the model is validated via comparisons with the experimental measurements in two different oceans. Satisfactory agreements with the measurements and with other computationally intensive classical physics-based models are demonstrated.Keywords:
transmission loss prediction; stratified acoustic model; geometrical ray tracing; phase shifts
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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MDPI and ACS Style
Wang, P.; Zhang, L.; Li, V.O.K. A Stratified Acoustic Model Accounting for Phase Shifts for Underwater Acoustic Networks. Sensors 2013, 13, 6183-6203.