<![CDATA[ IEEE Journal of Oceanic Engineering - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 48 2017July 27<![CDATA[Table of Contents]]>423C1C4153<![CDATA[Editorial Technical Communications Revisited]]>42350951035<![CDATA[Dynamic Modeling, Analysis, and Testing of a Variable Buoyancy System for Unmanned Multidomain Vehicles]]>4235115211776<![CDATA[Generating Vectored Thrust With the Rotational Paddling Gait of an ePaddle-EGM Mechanism: Modeling and Experimental Verifications]]>4235225311270<![CDATA[A Pressure Sensory System Inspired by the Fish Lateral Line: Hydrodynamic Force Estimation and Wall Detection]]>4235325432078<![CDATA[Simulation and Ship Detection Using Surface Radial Current Observing Compact HF Radar]]>4235445553703<![CDATA[Comparison of Spectral Estimation Methods for Rapidly Varying Currents Obtained by High-Frequency Radar]]>423556565828<![CDATA[Influence of Sea State and Tidal Height on Wave Power Absorption]]>4235665733515<![CDATA[Three-Dimensional Target Reconstruction From Multiple 2-D Forward-Scan Sonar Views by Space Carving]]>4235745892256<![CDATA[Automatic Detection of Marine Gas Seeps Using an Interferometric Sidescan Sonar]]>2, or from man-made constructions such as pipelines or well heads, and potentially also from subseafloor CO_{2} storage sites. Improved seep detection makes it possible to estimate the amount of greenhouse gases entering the oceans, and to promptly detect and address potential leaks to reduce environmental and economical consequences. Sonar is an excellent tool for seep detection due to the strong acoustic backscatter properties of gas-filled bubbles in water. Existing methods for acoustic seep detection include multibeam and sidescan surveying, as well as active and passive sensors mounted on a stationary platform. In this work, we develop a new method for automatic seep detection using an interferometric sidescan sonar. We apply signal processing techniques combined with knowledge about acoustical and spatial properties of seeps for improved detectability. The proposed method fills an important gap in existing technology—the ability to automatically detect a seep during a single pass with an autonomous underwater vehicle (AUV) equipped with an interferometric sidescan sonar. Results from simulations as well as field data from two leaking abandoned wells in the North Sea indicate that small seeps are consistently detected on a sandy seafloor even when the observation time is limited (a single pass with the AUV). We explore the detection capability for different seafloor types ranging from silt to gravel.]]>4235906025954<![CDATA[Kernel-Function-Based Models for Acoustic Localization of Underwater Vehicles]]>4236036181352<![CDATA[A Comprehensive Bottom-Tracking Method for Sidescan Sonar Image Influenced by Complicated Measuring Environment]]>$\pm$ 0.19 m of the standard deviation referenced to water depth at the points of measurement. Furthermore, taking the integration of the sounding data, the tidal level, and the towfish depth as reference, the proposed method obtains $\pm$0.17 m of the standard deviation. These parameters show that the proposed method has high accuracy in bottom tracking. Finally, this method is further discussed in the applications, and the results show that it has better performance than the traditional threshold method where the SSS waterfall image is influenced under complicated measurement environment. A shortcoming induced by the SSS -
maging mechanism is also found that this method may be invalid when high seabed targets lie at two sides of towfish nadir, which can be solved by increasing the towfish altitude.]]>4236196311366<![CDATA[Geoacoustic Inversion of Airgun Data Under Influence of Internal Waves]]>4236326381260<![CDATA[Optimal and Near-Optimal Detection in Bursty Impulsive Noise]]> $\alpha$-sub-Gaussian noise with memory order $m$ ($\alpha$SGN( $m$)) to model bursty impulsive noise. The model is based on the multivariate $\alpha$ -sub-Gaussian ($\alpha$SG) distribution family and statistically characterizes adjacent samples from elliptical distributions. The latter assumption holds well for snapping shrimp noise found in warm shallow underwater channels. We investigate the performance of conventional robust detectors in $\alpha$SGN($m$) and also propose novel near-optimal detectors. The Neyman–Pearson (NP) approach for binary hypothesis testing is considered and extensive simulation results for the aforementioned detectors are offered. For all instances, we employ an $\alpha$SGN($m$) proces-
whose parameters are tuned to snapping shrimp noise data sets. By incorporating good signal design rules, it is shown that there is a large performance gap between the new and conventional detectors in various impulsive regimes. Moreover, it is possible to derive a near-optimal detector if one only has information of the temporal statistics of the noise process.]]>4236396531977<![CDATA[Source Localization With Multiple Hydrophone Arrays via Matched-Field Processing]]>423654662792<![CDATA[Frequency Striations Induced by Moving Nonlinear Internal Waves and Applications]]>423663671617<![CDATA[Distortion of the Frequency Dependence of Bottom Attenuation $\alpha(f)$ Inverted From Modal Attenuation $\beta_{m}$ due to Bottom Model Mismatching]]>$c$ , density $\rho$, and attenuation $\alpha$) of the bottom play crucial roles in broadband acoustical propagation in shallow water. In general, these parameters and their profiles are very hard to measure directly, especially the bottom attenuation at low frequencies. A common way to get these parameters is inverting them from the acoustical field data collected by a hydrophone array. Since the true bottom environment is not known, most inversions assume an approximate bottom model, such as a single layer or a half-space. It has been recognized that the “model mismatching” impact is an important issue to be investigated. This work will discuss the distortion of the frequency dependence of the bottom attenuation inverted from the modal attenuation due to the bottom model mismatching. It is found that if an inaccurate layered bottom model is used for the inversion the intrinsic dispersion character of GA parameters will be contaminated under the constraint of the forced data fitting. An analytic expression of the distortion factor ${\mathfrak{D}_{\boldsymbol{m}}}(\boldsymbol{f})$ is obtained using perturbation theory and numerical simulation examples are presented to show how the waveguide dispersion behavior is partially transferred to the intrinsic dispersion of the medium attenuation. A simple way is also suggested to compensate for the distortion factor.]]>423672680710<![CDATA[Spatiotemporal Tracking of Ocean Current Field With Distributed Acoustic Sensor Network]]>4236816962308<![CDATA[Joint Power and Rate Control for Packet Coding Over Fading Channels]]>4236977101243<![CDATA[Frequency-Domain Turbo Equalization With Iterative Channel Estimation for MIMO Underwater Acoustic Communications]]>4237117211758<![CDATA[Software-Defined Underwater Acoustic Modems: Historical Review and the NILUS Approach]]>4237227371645<![CDATA[Spatially Distributed MIMO Sonar Systems: Principles and Capabilities]]>4237387512675<![CDATA[Corrections to "Efficient Adaptive Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications" [W. Duan, J. Tao, and Y. Rosa Zheng, IEEE J. Ocean. Eng ., 2017, DOI: 10.1109/JOE.2017.2707285]]]>42375275235