Surface and underwater acoustic target recognition using only two hydrophones based on machine learning

Abstract

Surface and underwater (S/U) acoustic targets recognition is an important application of passive sonar. It is difficult to distinguish them due to the mixture of underwater target radiation noise and marine environmental noise. In previous studies, although using a single hydrophone was able to identify S/U acoustic targets, there were still a few hydrophones that had poor accuracy. In this paper, S/U acoustic targets recognition using two hydrophones based on Gradient Boosting Decision Tree is proposed, and it is first found out as high as 100% accuracy could be achieved with the implementation of SACLANT 1993 data. The real experimental data are always rare and insufficient. The big training dataset is generated using environmental information by acoustic model named KRAKEN. Simulation and experimental data used in the model are heterogeneous, and the differences between these two kinds of data are assimilated by using vertical linear array feature extraction method. The model realizes the recognition of S/U acoustic targets based on channel information besides source spectrum information. By using the combination of two hydrophones, the surface and underwater targets recognition accuracy reached 1 and 0.9384, while they are only 0.4715 and 0.5620 using a single hydrophone, respectively.