Source Depth Estimation Based on Random Forest Approach Using Ocean Waveguide Data - Shen, Linglin; Sun, Xiangbo (Journal of Research in Science and Engineering, 2025)
In practice, the estimation of source localization based on matched field processing is significantly affected by environmental parameters, leading to the so-called mismatch problem. This paper models the sound source depth estimation problem as a classification issue in machine learning and discusses how the random forest method can be used to solve the depth estimation problem of sound sources. The paper uses the SWELLEX-96 sea trial environmental parameters and the Kraken model to generate ocean waveguide data received by a vertical line array at different depths of the sound source. After normalizing and extracting features from the generated ocean waveguide data, the random forest (RF) method is applied to estimate the depth of the sound source. The results indicate that the RF method is feasible for estimating the depth of sound sources.