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: The dynamic data transmission technology of an expendable current profiler is proposed in this paper. Two parallel varnished wires are employed as the data transmission medium. By testing the transmission properties of the varnished wires, a baseband transmission system is studied and designed. An optocoupler is used as the physical layer for data transmission. The data transmission protocol is modified and optimized in accordance with the RS232 protocol, and the Manchester code is superimposed. According to the results of indoor and marine tests, the data transmission distance of the designed system, which employed a 0.1-mm-diameter varnished wire, extends to 2 km with high accuracy for data transmission, exhibiting excellent performance. Moreover, this data transmission technology could be used for other expendable marine-environment parametric measuring instruments such as an expendable bathythermograph and expendable conductivity temperature depth profiler.

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: 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: 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: This paper investigates the capability of X -band radar systems to estimate nearshore bathymetry fields by considering both simulated and measured radar data. For the first time, a sensitivity analysis is performed to evaluate how sea-state conditions affect bathymetric estimates. For this purpose, sea wave fields generated by means of a numerical model, based on a nonlinear shallow-water equation solver, are used. Starting from the synthetic radar data, which represent the input of the bathymetric estimation algorithm, the bathymetric reconstruction is performed through the normalized scalar product (NSP) estimation strategy, exploiting a spatial partitioning of the radar data. In this way, it is possible to improve the accuracy of the estimates in nearshore areas, where the space-varying behavior of the sea depth and the presence of coastlines or coastal structures typically leads to a spatial inhomogeneity of the wave motion. In this regard, it is shown how the choice of the partitioning settings affects the bathymetric estimates obtained from high-resolution X -band radar images by using the NSP strategy. In addition, an adaptive partitioning strategy that takes into account the wave evolution in nearshore shallow waters is devised. Based on both simulated and measured radar data, the accuracy of the bathymetric estimates achievable through the proposed adaptive partitioning process and that obtained by exploiting the approach using uniform spatial partitioning are compared. The results obtained confirm the robustness of the NSP technique with respect to sea conditions and, moreover, demonstrate that the proposed adaptive partitioning strategy provides more accurate bathymetric estimates than those obtained with the space-invariant partitioning procedure.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Traditional constant false alarm rate (CFAR) detectors suffer probability of detection (PD) degradation influenced by the outliers such as interfering ship targets, side lobes, and ghosts, especially in crowded harbors and busy shipping lines. In this paper, a new two-parameter CFAR detector based on adaptively truncated clutter statistics (TS-LNCFAR) is proposed. The new two-parameter CFAR detector uses log-normal as the statistical model; by adaptive-threshold-based clutter truncation in the background window, the outliers are removed from the clutter samples, while the real clutter is preserved to the largest degree. The log-normal model is accurately built using the truncated clutter statistics through the maximum-likelihood estimator. Compared with traditional CFAR detectors, the parameter estimation is more accurate, and TS-LNCFAR has a better false alarm regulation property and a high PD in a multiple-target environment. Furthermore, the parameter estimation and threshold calculation do not need iterative numerical calculation, and TS-LNCFAR has a high computational efficiency. The superiority of the proposed TS-LNCFAR detector is validated on the multilook Envisat-ASAR and TerraSAR-X data.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.