LAUSR

The estimation of ocean sound speed profiles (SSPs) requires the inversion of an acoustic field using limited observations. Such inverse problems are underdetermined, and require regularization to ensure physically realistic solutions. The empirical orthonormal function (EOF) is capable of a very large compression of the data set. In this paper, the non-linear response of the sound pressure to SSP is linearized using a first order Taylor expansion, and the pressure is expanded in a sparse domain using EOFs. Since the parameters of the inverse model are sparse, compressive sensing (CS) can help solve such underdetermined problems accurately, efficiently, and with enhanced resolution. Here, the orthogonal matching pursuit (OMP) is used to estimate range-independent acoustic SSPs using the simulated acoustic field. The superior resolution of OMP is demonstrated with the SSP data from the South China Sea experiment. By shortening the duration of the training set, the temporal correlation between EOF and test sets is enhanced, and the accuracy of sound velocity inversion is improved. The SSP estimation error versus depth is calculated, and the 99% confidence interval of error is within ±0.6 m/s. The 82% of mean absolute error (MAE) is less than 1 m/s. It is shown that SSPs can be well estimated using OMP.