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Autonomous Maritime Navigation and Sensor Fusion
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Autonomous Maritime Navigation and Sensor Fusion

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7611

Special Issue Editors

Dr. Itzik Klein

E-Mail Website
Guest Editor
The Hatter Department of Marine Technologies, School of Marine Sciences, University of Haifa, Haifa 3498838, Israel
Interests: data-driven navigation; autonomous underwater vehicle navigation; accurate low-cost navigation solutions; cooperative navigation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aboelmagd Noureldin

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Royal Military College of Canada (RMCC) with Cross-Appointment at both the School of Computing and the Department of Electrical and Computer Engineering, Queen’s University, ON K7L 3N6, Canada
Interests: inertial navigation; global navigation satellite systems; GPS; wireless location; navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been a growing interest in autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs) for various types of applications including oceanographic surveys, scientific research, military-oriented applications, and structure monitoring. One of the most critical aspects of an autonomous vehicle is the navigation system. Therein, a wide range of sensor measurements are fused in the navigation filter to obtain the vehicle position, velocity, and orientation.

This Special Issue aims to collect high-quality research papers and review articles focusing on recent advances in autonomous maritime navigation and sensor fusion theory and applications.

Potential topics of interest include (but are not limited to):

  • Innovative autonomous navigation approaches;
  • Bio-inspired based navigation;
  • Nonlinear estimation for sensor fusion;
  • Geophysical navigation;
  • Unorthodox navigation architectures such as gyro-free or angular accelerometer-based configurations;
  • Cooperative navigation;
  • Acoustic navigation.

Dr. Itzik Klein
Prof. Dr. Aboelmagd Noureldin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Navigation
  • Sensor fusion
  • Nonlinear estimation
  • Inertial navigation system
  • Inertial measurement unit
  • Doppler velocity log
  • Terrain navigation
  • Underwater simultaneous localization and mapping

Published Papers (3 papers)

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Research

18 pages, 4490 KiB  
Article
Data-Driven Analysis for Safe Ship Operation in Ports Using Quantile Regression Based on Generalized Additive Models and Deep Neural Network
by Hyeong-Tak Lee, Hyun Yang and Ik-Soon Cho
Sensors 2021, 21(24), 8254; https://doi.org/10.3390/s21248254 - 10 Dec 2021
Cited by 4 | Viewed by 2384
Abstract
Marine accidents in ports can cause loss of human life and property and have negative material and environmental impacts. In South Korea, due to a pier collision accident of a large container ship in Busan New Port of South Korea, the need for [...] Read more.
Marine accidents in ports can cause loss of human life and property and have negative material and environmental impacts. In South Korea, due to a pier collision accident of a large container ship in Busan New Port of South Korea, the need for safe ship operation guidelines in ports emerged. Therefore, to support quantitative safe ship operation guidelines, ship trajectory data based on automatic information system information have been used. However, because this trajectory information is variable and uncertain due to various situations arising during a ship’s navigation, there is a limit to deriving results through traditional regression analysis. Considering the characteristics of these data, we analyzed ship trajectories through quantile regression using two models based on generalized additive models and neural networks corresponding to deep learning. Among the automatic information system information, the speed over ground, course over ground, and ship’s position were analyzed, and the model was evaluated based on quantile loss. Based on this study, it is possible to suggest safe operation guidelines for the position, speed, and course of the ship. In addition, the results of this work can be further developed as a manual for the in-port-autonomous operation of ships in the future. Full article
(This article belongs to the Special Issue Autonomous Maritime Navigation and Sensor Fusion)
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14 pages, 566 KiB  
Article
BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors
by Hadar Shalev and Itzik Klein
Sensors 2021, 21(13), 4457; https://doi.org/10.3390/s21134457 - 29 Jun 2021
Cited by 8 | Viewed by 2274
Abstract
Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an iterative [...] Read more.
Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an iterative least squares algorithm to estimate the unknown target position vector. Instead of using iterative least squares, this paper presents a deep-learning based framework for the bearing-only target tracking process, applicable for any bearings-only target tracking task. As a data-driven method, the proposed deep-learning framework offers several advantages over the traditional iterative least squares. To demonstrate the proposed approach, a scenario of tracking an autonomous underwater vehicle approaching an underwater docking station is considered. There, several passive sensors are mounted near a docking station to enable accurate localization of an approaching autonomous underwater vehicle. Simulation results show the proposed framework obtains better accuracy compared to the iterative least squares algorithm. Full article
(This article belongs to the Special Issue Autonomous Maritime Navigation and Sensor Fusion)
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18 pages, 1167 KiB  
Article
A Graph Localization Approach for Underwater Sensor Networks to Assist a Diver in Distress
by Roee Diamant and Roberto Francescon
Sensors 2021, 21(4), 1306; https://doi.org/10.3390/s21041306 - 11 Feb 2021
Cited by 3 | Viewed by 1921
Abstract
In this paper, we focus on the problem of locating a scuba diver in distress using a sensor network. Without GPS reception, submerged divers in distress will transmit SOS messages using underwater acoustic communication. The study goal is to enable the quick and [...] Read more.
In this paper, we focus on the problem of locating a scuba diver in distress using a sensor network. Without GPS reception, submerged divers in distress will transmit SOS messages using underwater acoustic communication. The study goal is to enable the quick and reliable location of a diver in distress by his fellow scuba divers. To this purpose, we propose a distributed scheme that relies on the propagation delay information of these acoustic SOS messages in the scuba divers’ network to yield a range and bearing evaluation to the diver in distress by any neighboring diver. We formalize the task as a non-convex, multi-objective graph localization constraint optimization problem. The solution finds the best configuration of the nodes’ graph under constraints in the form of upper and lower bounds derived from the inter-connections between the graph nodes/divers. Considering the need to rapidly propagate the SOS information, we flood the network with the SOS packet, while also using rateless coding to leverage information from colliding packets, and to utilize time instances when collisions occur for propagation delay evaluation. Numerical results show a localization accuracy on the order of a few meters, which contributes to quickly locating the diver in distress. Similar results were demonstrated in a controlled experiment in a water tank, and by playback data from a sea experiment for five network topologies. Full article
(This article belongs to the Special Issue Autonomous Maritime Navigation and Sensor Fusion)
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