Compressive sensing (CS) based estimation technique utilizes a sparsity promoting constraint and solves the direction-of-arrival (DOA) estimation problem efficiently with high resolution. In this paper a grid free CS based DOA estimation technique is proposed, which uses sequential multiple snapshot data. Conventional CS technique suffers from a basis mismatch issue, while grid free CS technique is relieved of basis mismatch problem. Moreover, when the DOAs are stationary, multiple snapshot processing provides stable estimates over fluctuating single snapshot processing results. For multiple snapshot processing, the generalized version of total variation norm (group total variation norm) is implemented to impose a common sparsity pattern of multiple snapshot solution vectors in a continuous angular domain. Furthermore, an extended version is proposed using the singular value decomposition technique to mitigate computational complexity resulting from a large number of multiple snapshots. Data from SWellEx-96 are used to examine the proposed method. From the experimental data, it was observed that the present method not only offers high resolution even when the sources are coherent, but also the basis mismatch in the conventional CS method can be avoided.

Underwater acoustics is the study of all phenomena related to the occurrence, propagation, and reception of sound waves in the water medium. Because electromagnetic waves undergo a significant attenuation in water, sound waves, which have relatively low propagation loss and high propagation speed, are used for underwater communication and detection. In the field of underwater acoustics, studies are mainly conducted on underwater communications, underwater target detection, marine resources, and measurement and analysis of underwater sound sources. Most applications for underwater acoustics can be described as remote sensing. Remote sensing is employed when an object, condition, or phenomenon of interest cannot be directly observed and information about the target of interest is acquired indirectly using data. In underwater acoustics, this can be described simply as a sound navigation and ranging (sonar) system. Sonar systems can be broadly classified into passive and active systems. Passive sonar systems acquire information by using sensors to measure the acoustic energy (signal) emitted by the target of interest. In active sonar systems, the observer obtains information by directly emitting an acoustic pulse and gathering the returning signals that are reflected by the target. Machine learning, which is widely known today, was initially used in academia for developing artificial intelligence. Recently, the use of machine learning has become widespread owing to the introduction of high-speed parallel computing that uses graphics processing units (GPUs) and can perform reliable learning based on big data, as well as develop various machine learning techniques that can find optimal solutions. Machine learning has contributed to the evolution of acoustic signal processing and voice recognition, and it is also utilized in various ways in the field of underwater acoustics. It is used for traditional remote sensing, such as in detection/classification of underwater sound sources and targets and localization. In addition, it is being used in the field of acoustic signal processing for seabed classification and marine environment information extraction and is producing an abundance of scientific results. Data-driven machine learning divides the data into a training set and test set. The training set is used to create a model that is suitable for machine learning, and the model's accuracy is increased through a repetitive model update process in which the model is validated via the

This paper describes a time delay estimation (TDE) technique using compressive sensing (CS) off the grid, which estimates the channel impulse response in a continuous time domain. The TDE can be formulated into a sparse signal reconstruction problem where the CS technique can be applied. Previous works have used standard finite dimensional CS with evenly discretized grids. However, the actual time delays will not always lie on the discrete grid, and this mismatch between the actual and discretized time delays results in reconstruction degradation. To overcome the basis mismatch, a TDE technique using an off the grid CS framework is proposed by modifying the scheme in the off the grid direction of arrival (DOA) estimation [Xenaki and Gerstoft, J. Acoust. Soc. Am. 137(4), 1923-1935 (2015)]. The effectiveness of the suggested method is demonstrated on real data from a water tank experiment. The off the grid CS TDE is shown to have super-resolution, which enables close arrivals to be distinguished.

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.

The compressive time delay estimation (TDE) is combined with delay-and-sum beamforming to obtain direction-of-arrival (DOA) estimates in the time domain. Generally, the matched filter that detects the arrivals at the hydrophone is used with beamforming. However, when the ocean noise smears the arrivals, ambiguities appear in the beamforming results, degrading the DOA estimation. In this work, compressive sensing (CS) is applied to accurately evaluate the arrivals by suppressing the noise, which enables the correct detection of arrivals. For this purpose, CS is used in two steps. First, the candidate time delays for the actual arrivals are calculated in the continuous time domain using a grid-free CS. Then, the dominant arrivals constituting the received signal are selected by a conventional CS using the time delays in the discrete time domain. Basically, the compressive TDE is used with a single measurement. To further reduce the noise, common arrivals over multiple measurements, which are obtained using the extended compressive TDE, are exploited. The delay-and-sum beamforming technique using refined arrival estimates provides more pronounced DOAs. The proposed scheme is applied to shallow-water acoustic variability experiment 15 (SAVEX15) measurement data to demonstrate its validity.