3D Underwater Environment Research Articles

Coverage‐aware connectivity‐constrained unattended sensor deployment in underwater acoustic sensor networks

AbstractCareful deployment of nodes in underwater acoustic sensor networks in a distributed manner with the goal of maximized coverage and guaranteed connectivity is a challenging problem because it is very difficult and costly to access the 3D underwater environment. This paper presents a novel algorithm for self‐deployment of nodes in underwater acoustic sensor networks assuming that the nodes are randomly dropped to the water surface and form a densely populated connected network at the water surface. The idea of the algorithm is based on calculating an optimized depth for each node in the network in such a way that the possible sensing coverage overlaps are minimized and the connectivity of final topology is guaranteed. The algorithm has three main phases. In the first phase, nodes are organized in a tree structure that is rooted at the surface station. In the second phase, the depths for all nodes are computed iteratively at surface station. In the final phase, the calculated depths are distributed to nodes so that the nodes start sinking. The performance of the proposed approach is validated through simulation. We observed that the proposed approach performs at least 10% better in terms of network coverage than contemporary schemes in the literature. Copyright © 2016 John Wiley & Sons, Ltd.

An Open-source Bio-inspired Solution to Underwater SLAM

We present a bio-inspired approach to deal with the localization and spatial mapping problem, extending the successful previous RatSLAM approach from 2D ground vehicles to the 3D underwater environments. Our approach, called DolphinSLAM, is a SLAM system based on mammals navigation. Experiments in simulation and real environments were conducted involving long-term navigation tasks with different robots and sensors. Our proposal is open- source, being integrated with the Robot Operating System (ROS).

Sensitivity analysis of three-dimensional sound pressure fields in complex underwater environments

A sensitivity kernel for sound pressure variability due to variations of index of refraction is derived from a higher-order three-dimensional (3D) split-step parabolic-equation (PE) solution of the Helmholtz equation. In this study, the kernel is used to compute the acoustic sensitivity field between a source and a receiver in a 3D underwater environment, and to quantify how much of the medium change can cause significant consequence on received acoustic signals. Using the chain rule, the dynamics of sensitivity fields can be connected to the dynamics of ocean processes. This talk will present numerical examples of sound propagation in submarine canyons and continental slopes, where the ocean dynamics cause strong spatial and temporal variability in sound pressure. Using the sensitivity kernel technique, we can analyze the spatial distribution and the temporal evolution of the acoustic sensitivity fields in these geologically and topographically complex environments. The paper will also discuss other applications of this sound pressure sensitivity kernel, including uncertainty quantification of transmission loss prediction and adjoint models for 3D acoustic inversions. [Work supported by the ONR.]

Multitarget Detection and Estimation Based on Passive Multilateral TDOAs of Transient Signals

A modified deterministic annealing expectation maximization (MDAEM) algorithm has been proposed [D. Carevic, ldquoAutomatic estimation of multiple target positions and velocities using passive TDOA measurements of transients,rdquo IEEE Transactions on Signal Processing, vol. 55, no. 2, pp. 424-436, 2007] for the estimation of the motion parameters of multiple targets moving in cluttered three-dimensional (3D) underwater environments. In many situations, the number of targets in the measurements batch is not known, and the MDAEM algorithm is run for an inexact (guessed) number of the motion models, where, usually, the guessed number of models is larger than the true number of targets. This correspondence describes a detection procedure that determines which of the motion models estimated by the algorithm are related to the true targets in the measurements batch.