An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture

Abstract

Military forces of every country are trying their best to protect their motherland from the attackers. With advancement in marine technology, it has become critical to detect and track the target by obtaining active measurements before it is close enough to attack. The utilization of unmanned underwater vehicles for target tracking behavior is gaining great attention due to continuous advancement of underwater vehicular technology. Nevertheless, safe and stable communications issues among different acoustic devices are still under active investigation to reach a robust, secure, and flexible underwater networking. Moreover, due to harsh underwater environment, acoustic simulations are also time-consuming; therefore, an accurate model for target detection and tracking is a necessity. Apart from the harsh environment of underwater networks, various technologies emerging for terrestrial networking are also becoming the part of underwater networking. For instance, cognitive acoustic networks, software-defined networks, network function virtualization, cloud computing, fog computing, and internet of underwater things; all are leading to trusted next-generation underwater networks. In this paper, we first provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking that answers several questions regarding the current necessities of underwater networks and finally, provides a solution that opens several doors for research community to excel in this area.INDEX TERMS Internet of underwater things (IoUT), ray tracing models, underwater acoustic networks (UAN), unmanned underwater vehicles (UUV).

Hybrid FLC-LMS Algorithm for Predicting Sediment Volume in the River

“…Finally, it provides a solution that opens several doors for community research to excel in this area. [5].…”

Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations

“…Without aiming to write an exhaustive review in the area of the underwater target tracking, we should mention the following works [11][12][13]. The analysis of these comprehensive contributions together with other related literature allows for determining the factors and restrictions which affect the performance in any specific applied tracking problem:…”

“…Basic CMNF: the conditionally minimax nonlinear filter (11) and (12), which uses the combined bearing and Doppler measurements Y bd k = col({(ξ i k , η i k , ω i k )}, defined by (8). • CMNF LS: the conditionally minimax nonlinear filter (11) and (12), which uses the original bearing measurements and the LS estimate…”

“…CMNF LS with Doppler: the conditionally minimax nonlinear filter (11) and (12), which uses the combined measurements and the LS estimate…”

A Glider-Assist Routing Protocol for Underwater Acoustic Networks With Trajectory Prediction Methods

“…At present, with the exception of sensor nodes, gliders equipped with various sensors can also observe and obtain a lot of information, such as temperature, salinity, and ocean current changes, which play a vital role in marine exploration, resource development, and disasters preventing, etc. [3]. In the hybrid network containing sensor nodes and gliders, many sensor nodes can detect that the environment is passively moving with the flow of water.…”