A three-dimensional broadband underwater acoustic concentrator

We propose a three-dimensional (3D) omnidirectional underwater acoustic concentrator based on the concept of acoustic prison, which can realize a substantial enhancement of underwater sound signals in broadband ranges. This device mainly employs the non-resonant multiple reflection characteristics of the semi-enclosed geometric space, so it has a wide working frequency bandwidth. Compared with the previous reported concentrators based on transform acoustics mechanism, the structure is more simple, and most importantly, it can realize omnidirectional signal enhancement in 3D space. Moreover, the working frequency band of this acoustic concentrator depends on the size of the concentrator, so it can be changed directly through a size scaling, which is convenient for engineering applications. In general, the designed underwater acoustic concentrator has the advantages of simple structure, scalability and large bandwidth of working frequency, and high signal gain. It has potential application values in underwater target detection and other aspects.

Multiple Bio-Inspired Father–Son Underwater Robot for Underwater Target Object Acquisition and Identification

Underwater target acquisition and identification performed by manipulators having broad application prospects and value in the field of marine development. Conventional manipulators are too heavy to be used for small target objects and unsuitable for shallow sea working. In this paper, a bio-inspired Father–Son Underwater Robot System (FURS) is designed for underwater target object image acquisition and identification. Our spherical underwater robot (SUR), as the father underwater robot of the FURS, has the ability of strong dynamic balance and good maneuverability, can realize approach the target area quickly, and then cruise and surround the target object. A coiling mechanism was installed on SUR for the recycling and release of the son underwater robot. A Salamandra-inspired son underwater robot is used as the manipulator of the FURS, which is connected to the spherical underwater robot by a tether. The son underwater robot has multiple degrees of freedom and realizes both swimming and walking movement modes. The son underwater robot can move to underwater target objects. The vision system is installed to enable the FURS to acquire the image information of the target object with the aid of the camera, and also to identify the target object. Finally, verification experiments are conducted in an indoor water tank and outdoor swimming pool conditions to verify the effectiveness of the proposed in this paper.

Underwater Target Signal Classification Using the Hybrid Routing Neural Network

In signal analysis and processing, underwater target recognition (UTR) is one of the most important technologies. Simply and quickly identify target types using conventional methods in underwater acoustic conditions is quite a challenging task. The problem can be conveniently handled by a deep learning network (DLN), which yields better classification results than conventional methods. In this paper, a novel deep learning method with a hybrid routing network is considered, which can abstract the features of time-domain signals. The used network comprises multiple routing structures and several options for the auxiliary branch, which promotes impressive effects as a result of exchanging the learned features of different branches. The experiment shows that the used network possesses more advantages in the underwater signal classification task.

Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

Measure of nonlinearity for underwater target tracking using hull-mounted sensor

PurposeDoppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.Design/methodology/approachThe system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.FindingsIn this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.Originality/valueAlgorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

Perturbation Propagation Models for Underwater Sensor Localisation using Semidefinite Programming

Real time Underwater sensor networks (UWSNs) suffer from localisation issues due to a dearth of incorporation of different geometric scenarios in UWSN scenarios. To address these issues, this paper visualises three specific scenarios of perturbation. First, small sized and large numbered particles of perturbance moving in a tangential motion to the sensor nodes; second, a single numbered and large-sized particle moving in a rectilinear motion by displacing the sensor nodes into sideward and forward direction, and third, a radially outward propagating perturbance to observe the influenced sensor nodes as the perturbance moves outwards. A novel target localisation and tracking is facilitated by including marine vehicle navigation as a source of perturbation. Using semidefinite programming, the proposed perturbation models minimise localisation errors, thereby enhancing physical security of underwater sensor nodes. By leveraging the spin, cleaving motion and radial cast-away behaviour of underwater sensor nodes, the results confirm that the proposed propagation models can be conveniently applied to real time target detection and estimation of underwater target nodes.