Sea surface fluctuation by the wind has an effect on the performance of underwater communication systems since it induces time-variant scattering. Such scattering gives the time spread and consequently the signal fading in the amplitude and phase of the transmitted signal. In this study, such signal fading is examined experimentally on the basis of frequency and temporal coherence variations under wind speeds of about 15 -16 kn at a 15.5 m height from the sea level. The frequency coherence bandwidth of surface reflection scattered signals is found to be about 300 Hz at a 3 dB bandwidth. The mean coherence is about 0.9 for a grazing angle of 16 and at wind speed of 15 -16 kn. The dominant coherence variation frequency is consistent with the dominant sea surface wave frequency.

Acoustic Doppler current profilers (ADCPs) were developed to acquire water current velocities, as well as depth-dependent echo intensities. As the backscattering strength of an underwater object can be estimated from the measured echo intensity, the ADCP can be used to estimate plankton populations and distributions. In this study, the backscattering strength of bubble clusters in a water tank was estimated using the commercial ADCP as a proof-of-concept. Specifically, the temporal variations in the backscattering strength and the duration of bubble existence were quantitatively evaluated. Additionally, the PDSL (population density spectrum level) and VF (void fraction) of the artificial bubbles were characterized based on the obtained distribution characteristics using a PDPA (phase Doppler particle analyzer).

Recently, neural network-based deep learning techniques have been actively applied to detect underwater objects in sonar (sound navigation and ranging) images. However, unlike optical images, acquiring sonar images is extremely time- and cost-intensive, and therefore securing sonar data and conducting related research can be rather challenging. Here, a side-scan sonar was used to obtain sonar images to detect underwater objects off the coast of the Korean Peninsula. For the detection experiments, we used an underwater mock-up model with a similar size, shape, material, and acoustic characteristics to the target object that we wished to detect. We acquired various side-scan sonar images of the mock-up object against the background of mud, sand, and rock to account for the different characteristics of the coastal and seafloor environments of the Korean Peninsula. To construct a detection network suitable for the obtained sonar images from the experiment, the performance of five types of feature extraction networks and two types of optimizers was analyzed. From the analysis results, it was confirmed that performance was achieved when DarkNet-19 was used as the feature extraction network, and ADAM was applied as the optimizer. However, it is possible that there are feature extraction network and optimizer that are more suitable for our sonar images. Therefore, further research is needed. In addition, it is expected that the performance of the modified detection network can be more improved if additional images are obtained.

To study the bottom reflection of underwater acoustic sound in a bottom-limited propagation environment, an experiment was conducted using four transmitting sounds in the form of a continuous wave from 1 to 6 kHz. The site of the experiment was a continental shelf region off the east coast of Korea where the bottom was composed of sandy mud. The mean water depth was 1100 m in the experiment area. Oceanographic data and acoustic data were collected simultaneously during the experiment. It was found that the sound pressure level decreased by 90 dB to 3.4 km and there is little frequency dependence because a strong direct path contributes more than a bottom-reflected path in sound pressure level. At a range between 6 and 7 km, there is a strong bottom-reflected ray path and frequency dependence exists because the bottom reflection loss varies with frequency at a given grazing angle. Sound pressure levels increase as the range increases between 6 and 7 km by 5.4, 1.9, 1.7, and 1.5 dB at frequencies of 1000, 2490, 3990, and 5490 Hz, respectively.

Bubble clusters present in seawater can cause acoustic interference and acoustic signal distortion during marine exploration. However, this interference can also be used as an acoustic masking technique, which has significant implications for military purposes. Therefore, characterizing the distribution of bubble clusters in water would allow for the development of anti-detection technologies. In this study, a sea experiment was performed using a multi-sonar array system and a bubble-generating material developed by our research group to obtain acoustic signals from an artificial bubble cluster and characterize its distribution. The acquired acoustic data were preprocessed, and reverse-time migration (RTM) was applied to the dataset. For effective RTM, an envelope waveform was used to decrease computation time and memory requirements. The envelope RTM results could be used to effectively image the distribution characteristics of the artificial bubble clusters. Compared with acoustic Doppler current profiler data, the backscattering strength of the boundary of the imaged artificial bubble cluster was estimated to range between −30 and −20 dB. Therefore, the three-dimensional distribution characteristics of bubble clusters in the open sea can be effectively determined through envelope RTM. Furthermore, the data obtained from this study can be used as a reference for future studies.