Using the trapped energy ratio for source depth discrimination with a horizontal line array: Theory and experimental results

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

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.

Passive source depth estimation using beam intensity striations of a horizontal linear array in deep water

Source depth estimation is an important yet very difficult task for passive sonars, especially for horizontal linear arrays (HLAs). This paper proposes an efficient two-step depth estimation scheme using narrowband and broadband constructive and deconstructive striation patterns due to interference between the direct (D) and sea surface reflected (SR) arrivals at an HLA on the bottom of deep water. First, the horizontal source-array ranges are derived from triangulation results of solid angle estimates by subarray beamforming. The applicable areas of the method in deep water are investigated through Mento Carlo simulations, assuming different subarray partitioning ways of a given HLA aperture. Second, cost functions are built to match the measured beam intensity striations with modeled ones. To mitigate the spatial smoothing effect of the beam intensity striations during beamforming, a criterion of the largest subarray aperture is established, and a computationally efficient way is presented to model the replicas by the D-SR time delay templates at a single element of the array calculated by ray theory. The performance degradation due to limited source range spans, the distortion of the beam intensity striations, and range estimation errors has been analyzed. Two experimental datasets verify the effectiveness of the proposed method.

Generalized likelihood ratio detector with horizontal linear array in presence of interference in uncertain shallow-water environment

Environmental uncertainties and interference are the main factors affecting the detection performance for detection problems in a shallow-water environment. To obtain robust performance, an interference and environmental uncertainties-constrained generalized likelihood ratio detector (IEU-GLRD) is proposed based on a horizontal linear array (HLA). The uncertainty sets of signal and interference wavefronts are used by IEU-GLRD, which contain different uncertainties when the interference source bearing relative to the HLA is known a priori. Due to the difference in the uncertainties, the signal, which is not in the uncertainty set of the interference, can be detected, while the interference is suppressed under different environmental parameters. The performance of IEU-GLRD is robust when the signal wavefront is approximately orthogonal to any interference wavefronts. The interference immunity of IEU-GLRD is mainly determined by the bearing of the interference source and the sediment sound speed, which is stronger when the interference source tends to the broad side and the sediment sound speed is lower.

Surface and Underwater Acoustic Source Discrimination Based on Machine Learning Using a Single Hydrophone

In shallow water, passive sonar usually has great difficulty in discriminating a surface acoustic source from an underwater one. To solve this problem, a supervised machine learning method using only one hydrophone is implemented in this paper. Firstly, simulated training data are generated by a normal mode model KRAKEN with the same environment setup as that in SACLANT 1993 experiment. Secondly, the k-nearest neighbor (kNN) classifiers are trained and evaluated using the scores of precision, recall, F1 and accuracy. Thirdly, the random subspace kNN classifiers are finely trained on three hyperparameters (the number of nearest neighbors, the number of predictors selected at random and the number of learners in the ensemble) to obtain the best model. Fourthly, a deep learning method called ResNet-18 is also applied, and it reaches the best balance between precision and recall, while the accuracies of both simulation and experimental data are all 1.0. Further, data from all 48 hydrophones of the vertical linear array (VLA) are analyzed using the three kinds of machine learning methods (kNN, random subspace kNN and ResNet-18) separately, and the results are compared. It is concluded that the performance of random subspace kNN is the best. Both the simulation and experimental results suggest the feasibility of machine learning as a surface and underwater acoustic source discrimination method even with only a single hydrophone.

Normal mode energy estimation based on reconstructing the incoherent beamformed outputs from a horizontal array

The acoustic pressure field in many underwater environments is well described by a superposition of normal modes. The normal modes can be used for source localization and environmental inversion. However, the wavenumber resolution of traditional normal mode filtering methods for a small-aperture horizontal array is usually not sufficient to identify individual modes in a shallow water waveguide. This paper proposes an original method of normal mode energy estimation to remove the energy leakage between modes. The modal energy is defined as the square of the modal amplitude. This method is to reconstruct the incoherent beamformed outputs in wavenumber domain for a horizontally moving source. The adaptive beamforming is used to suppress interference and improve output signal-to-noise ratio. The uncertainty of modal phase velocity has also been considered in this method. The proposed method can provide more accurate estimates of modal energy for a small-aperture horizontal array than the traditional mode filtering methods, such as the matched filter, the least squares mode filter, the regularized-least squares mode filter, and the maximum a posteriori mode filter, in simulations and experiments.

Use of multipath time-delay ratio for source depth estimation with a vertical line array in deep water

In this paper, a method for the problem of depth estimation of a broadband source via reliable acoustic path propagation is presented for the case using a vertical line array (VLA). The estimates are determined by two kinds of multipath time-delay ratios, namely, the ratio of direct-surface-reflected (D-SR) to direct-direct time-delays and the ratio of D-SR to surface-reflected-surface-reflected time-delays. The innovation of ratio behavior is that it provides a mechanism for obtaining a useful depth interval with the assumption of plane-wave propagation. The estimation accuracy of a depth interval relies on the degree to which the actual acoustic propagation characteristic can be modeled by image theory. Furthermore, the variability of depth interval due to the approximation made in the derivation method allows one to achieve binary discrimination of both the source depth and source range with only a minimal amount of prior environmental knowledge. The methodology of multipath time-delay estimation is first reviewed and improved, followed by an illustration of the source depth estimation and a discussion of the performance analysis using results from numerical simulations. Finally, the proposed method is demonstrated with experimental data collected in the South China Sea in which a short-aperture VLA is deployed near the sea bottom.

Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning

Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.