Description: Widely used conductivity-based salinity measurements may be affected by suspended sediments. Two sets of experiments were carried out to analyze the effects of sediment concentration and grain size on salinity measurements. The results show that the salinities measured by conductivity-temperature-depth probes in turbid saltwater are all lower than those measured in clear water. The absolute values of the difference in salinity between turbid saltwater and clear saltwater increase with increasing reference salinity and suspended sediment concentration (SSC). Moreover, the finer the sediment particles are, generally, the greater is their influence upon salinity measurement. For an SSC of 70 g/L and a reference salinity of 30 g/kg, the absolute values of salinity difference were 1.882, 1.675, and 1.285 PSU for the fine, median, and coarse sediment particles, respectively. These differences are attributed to blocking, occupying, adsorption, and flocculation effects.

Description: A set of image preprocessing approaches are developed for processing plankton images reconstructed from digital holograms. First, a threshold-based algorithm of image segmentation is proposed and applied to extract the regions of plankton from the original digital images. To improve the performance of image segmentation, an appropriate filter is adopted to reduce the background noise from the image and the image gray level is adjusted to enhance the image contrast. Second, we develop a novel and efficient edge detection method purposefully for the binary images. Third, we propose and use a simple chain-code-based algorithm to eliminate the single-pixel branches along the shape boundary, which will help boundary tracing work stably. Then, an algorithm is improved and applied to trace the boundaries of the plankton regions. This algorithm is optimized based on the relationship between two consecutive chain-codes such that it is fast on implementation. Finally, break points of the shape boundary are efficiently detected based on chain-codes and the boundary is represented compactly by a polygon comprised of those points. After images are preprocessed by these approaches, some redundant information of shape is reduced that will accelerate the running speeds of further image processing and aid identification and classification of plankton at species level. We analyze the accuracy and efficiency of our algorithms. The results show that our algorithm of image segmentation has a good performance in accuracy. Our edge detection method also outperforms the commonly used edge detection methods in terms of localization performance and the running time.

Description: Shipboard electric propulsion systems experience large power and torque fluctuations on their drive shaft due to propeller rotational motion and waves. This paper explores new solutions to address these fluctuations by integrating a hybrid energy storage system (HESS) and exploring energy management (EM) strategies. The HESS combines battery packs with ultracapacitor banks. Two strategies for real-time EM of HESS are considered: one splits the power demand such that high- and low-frequency power fluctuations are compensated by ultracapacitors and batteries, respectively; another considers the HESS as a single entity and designs an EM strategy to coordinate the operations of the ultracapacitors and batteries. For both strategies, model predictive control is used to address power tracking and energy saving under various operating constraints. To quantitatively analyze the performance of HESS and its associated controls, a propeller and ship dynamic model, which captures the underlying physical behavior, is established to support the control development and system optimization. Power fluctuation mitigation and HESS loss minimization, the main objectives, are evaluated in different sea conditions. Simulation results show that the coordination within HESS provides substantial benefits in terms of reducing fluctuations and losses.

Description: One intriguing property that is exploited by ocean acoustic tomography (OAT) is that acoustic signals travel in a multipath. As the first step of OAT, each raypath should be identified with a particular travel time. However, the set of multipath rays generated by an emitted signal is correlated or coherent, as they are produced by reflection and or by refraction in propagation. In this paper, a high-resolution method called smoothing multiple signal classification active large band (MUSICAL) is presented in the context of shallow-water OAT for separating coherent or fully correlated raypaths in the direction-of-arrival temporal domain. The method is a combination of the MUSICAL and spatial-frequency smoothing processing. Furthermore, the performance of smoothing-MUSICAL is illustrated by experiments based on both synthetic data and real data. This algorithm largely improves separation performances and presents fewer artifacts compared with conventional beamforming. In particular, experimental results show that smoothing-MUSICAL is more robust than beamforming facing a noisy environment with moderate signal-to-noise ratio.

Description: The sparse Bayesian learning based relevance vector machine (SBLRVM) algorithm is a promising algorithm to estimate the directions-of-arrival (DOAs) of multiple narrowband signals. The parameters involved in the DOA estimation model are automatically estimated by the algorithm that makes it more attractive than the deterministic sparsity based DOA estimation algorithms in which fine-tuning of parameters is necessary. However, one limitation of the algorithm is that it assumes the DOAs of the signals to be exactly aligned with the angular grids, which may not be true in practice. In this paper, we first propose an off-grid version of the narrowband SBLRVM algorithm. Next, we propose an off-grid wideband SBLRVM algorithm. The algorithms assume that the true scenario DOAs of the signals are not exactly aligned with the angular grids and the parameters of the algorithms are automatically estimated by the expectation maximization approach. In the wideband DOA estimation algorithm, we estimate one spatial power spectrum by simultaneously exploiting sparsity from all frequency bins. We demonstrate the application of the proposed algorithms by analyzing data from the shallow water HF $mathbf {97}$ ocean acoustic experiment. The estimated DOAs of a narrowband tonal from the experiment by using our proposed narrowband DOA estimation algorithm are consistent with the nonadaptive conventional beamformer. Processing a wideband chirp from the experiment shows that estimating one spatial power spectrum by simultaneously exploiting sparsity from all frequency bins using the proposed wideband DOA estimation algorithm is a more valuable processor than an incoherent combination of the power spectra from the individual frequency bins estimated using the proposed narrowband DOA estimation algorithm. Moreover, since our proposed algorithms are off-grid algorithms, an empirical analysis for the choice of the discreti- ation interval of the angular spread is not required as opposed to the on-grid DOA estimation algorithms. This results in a reduced computational complexity.

Description: The statistics of the mode space detector (MSD) whose modes are characterized by some degree of mismatch to truth [that is, the mismatched mode space detector (MMSD)] are derived. As a measure of the “containing relation” between the mode spaces derived from different environmental realizations we propose the relative projection error metric, through which the performance of the MMSD can be further investigated and compared to the MSD using accurate environmental knowledge. From this performance analysis, we suggest that by simply using the greatest dimensional physically supported mode space, we can obtain a robust MSD. It can achieve the same performance robustness as the energy detector—the performance is only determined by the array signal energy and is insensitive to the specific source position and environmental conditions. It can also avoid the potentially-significant performance degradation of the Bayesian detector when applied to certain deterministic scenarios at a cost of small average performance degradation. Numerical simulations in a typical uncertain shallow-water environment support our assertions.

Description: In March 2014, an Arctic Line Arrays System (ALAS) was deployed as part of an experiment in the Beaufort Sea (approximate location 72.323 N, 146.490 W). The water depth was greater than 3500 m. The background noise levels in the frequency range from 1 Hz to 25 kHz were measured. The goal was to have a three-dimensional sparse array that would allow determination of the direction of sound sources out to hundreds of kilometers and both direction and range of sound sources out to 1–2 km from the center of the array. ALAS started recording data at 02:12 on March 10, 2014 (UTC). It recorded data nearly continuously at a sample rate of 50 kHz until 11:04 on March 24, 2014. Background noise spectral levels are presented for low and high floe-drift conditions. Tracking/characterization results for ice-cracking events (with signatures typically in the 10–2000-Hz band), including the initiation of an open lead within about 400 m of the array, and one seismic event (with a signature in the 1–40-Hz band) are presented. Results from simple modeling indicate that the signature of a lead formation may be a combination of both previously hypothesized physics and enhanced emissions near the ice plate critical frequency (where the flexural wave speed equals that of the water sound speed). For the seismic event, the T-wave arrival time results indicate that a significant amount of energy coupled to T-wave energy somewhere along the path between the earthquake and ALAS.

Description: Horizontal line arrays are often used in underwater environments to detect/separate a weak signal and estimate its direction of arrival from many loud interfering sources and ambient noise. Conventional beamforming is robust but suffers from fat beams and high-level sidelobes. High-resolution beamforming, based on the inverse of the signal covariance matrix, such as minimum-variance distortionless response (MVDR), yields narrow beamwidths and low sidelobe levels but is sensitive to signal mismatch and requires many snapshots of data. This paper applies a deconvolution algorithm used in image deblurring to the conventional beam power of a uniform line array (spaced at half-wavelength) to avoid the instability problems of common deconvolution methods. The deconvolved beam power yields narrow beams, and low sidelobe levels similar to or better than high-resolution beamforming and at the same time retains the robustness of conventional beamforming. It yields a higher output signal-to-noise ratio than conventional (and MVDR) beamforming for isotropic noise. Performance is evaluated with simulated and real data.

Description: Underwater acoustical communication channels are characterized by the spreading of received signals in space (direction of arrival) and in time (delay). The spread is often limited to a small number of space-time clusters. In this paper, the space-time clustering is exploited in a proposed receiver designed for guard-free orthogonal frequency-division multiplexing with superimposed data and pilot signals. For separation of space clusters, the receiver utilizes a vertical linear array (VLA) of hydrophones, whereas for combining delay-spread signals within a space cluster, a time-domain equalizer is used. We compare a number of space-time processing techniques, including a proposed reduced-complexity spatial filter, and show that techniques exploiting the space-time clustering demonstrate an improved detection performance. The comparison is done using signals transmitted by a moving transducer, and recorded on a 14-element nonuniform VLA in sea trials at distances of 46 and 105 km.

Description: In this study, typhoon monitoring is addressed using X-band synthetic aperture radar (SAR) imagery collected by the German TerraSAR-X mission and the Italian COSMO-SkyMed constellation during the typhoon Megi. Geometrical features, rain rate, and wind speed associated with the typhoon are retrieved by the SAR data set. One of the key benefits of the X-band observations relies in their sensitivity to rain that can be exploited to provide an estimate of geometrical features and rain rate by analyzing attenuation bands present in the SAR data. In addition, wind speed is retrieved using a rain-free model based on two geophysical model functions (GMFs) and experimental results show that the nonlinear relationship between normalized radar cross section and wind speed provided by one of the GMFs can be exploited to provide a rough estimate of high wind speeds.