In underwater acoustics, source localization is often solved with matched-field processing (MFP), which, however, the MFP is limited in some applications due to its sensitivity to the mismatch between model-generated replica fields and measurements.
Recently, NIU Haiqiang and his colleagues from USA present a source localization method based on machine learning. It is a data-driven method that learns source ranges directly from observed acoustic data.
In that paperBy constructing a normalized sample covariance matrix, the pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural network (FNN), support vector machine (SVM), and random forest (RF). Then range estimation problem is solved both as a classification problem and as a regression problem.
To demonstrate the performance of thise machine learning methods, the results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing. Figure 1 shows the best localization results on two data sets by the FNN, SVM, and RF classifiers with 66 frequencies. The corresponding lowest mean absolute percentage error (MAPE) is about 2-3% for these three methods.
For further comparison, the results for classifiers, regressors and conventional MFPs are summarized TABLE I. The lowest MAPE is achieved by the SVM classifier, with 2% on both data sets.Overall, the performance of these three machine learning algorithms is are comparable when solving range estimation as a classification problem, and outperforms the conventional MFPs and regressors.
Fig.1 Source localization as a classification problem. Range predictions on Test-Data-1 (a, b, c) and Test-Data-2 (d, e, f) by FNN, SVM and RF for 300-950 Hz with 10 Hz increment, i.e., 66 frequencies. (a),(d) FNN classifier, (b),(e) SVM classifier, (c),(f) RF classifier. (Image by NIU).
NIU Haiqiang, Emma Reeves, Peter Gerstoft. Source Localization in an Ocean Waveguide Using Supervised Machine Learning. Journal of the Acoustical Society of America. (vol. 142, no. 3, pp. 1176-1188, September 2017). DOI: 10.1121/1.5000165.
Institute of Acoustics, Chinese Academy of Sciences, 100190 Beijing, China