Compressive Sound Speed Profile Inversion Using Beamforming Results

Abstract

Sound speed profile (SSP) significantly affects acoustic propagation in the ocean. In this work, the SSP is inverted using compressive sensing (CS) combined with beamforming to indicate the direction of arrivals (DOAs). The travel times and the positions of the arrivals can be approximately linearized using their Taylor expansion with the shape function coefficients that parameterize the SSP. The linear relation between the travel times/positions and the shape function coefficients enables CS to reconstruct the SSP. The conventional objective function in CS is modified to simultaneously exploit the information from the travel times and positions. The SSP is estimated using CS with beamforming of ray arrivals in the SWellEx-96 experimental environment, and the performance is evaluated using the correlation coefficient and mean squared error (MSE) between the true and recovered SSPs, respectively. Five hundred synthetic SSPs were generated by randomly choosing the SSP dictionary components, and more than 80 percent of all the cases have correlation coefficients over 0.7 and MSE along depth is insignificant except near the sea surface, which shows the validity of the proposed method.

Underwater Acoustic Research Trends with Machine Learning: Ocean Parameter Inversion Applications

“…As observed from these cases, as in the case of problems of inversion of underwater acoustics, the most advanced signal processing theory was first applied to improve the localization performance of underwater sources in combination with MFP. It was then applied to the inversion of ocean parameters such as marine sediment properties and sound speed profile (Yardim et al, 2014;Bianco and Gerstoft, 2016;Choo and Seong, 2018). An example of a subsequent related study is sound speed profile inversion using dictionary learning which is an unsupervised machine learning technique.…”

Underwater sound speed profile parameters estimation in asynchronous network

“…This process is time consuming and also requires a lot of labor, hence, it is not suitable for the applications that require fast and three dimensional estimation of sound speed profile in a new area. There exist some sound speed profile inversion based works proposed to obtain the sound speed profile 19,20 . In inversion based methods, synthetic acoustic data at the sensors of array are produced in accordance with the sound speed profile using underwater sound propagation model, and an objective function is defined to examine the similarity between the synthetic and the measured data 19 .…”

Sound velocity profile estimation using ray tracing and nature inspired meta‐heuristic algorithms in underwater sensor networks

“…Jain and Ali [14], proposed a method for estimating sound speed profiles from surface parameters using artificial neural networks, where the surface parameters are observed in a constant hourly time series using satellites. Recent works on SVP estimation using compressive sensing techniques have been proposed in [15, 16]. The hydrophone measurements were used to model the SVP using shape functions and compressive sensing techniques were applied further to estimate the SVP.…”