System for surveillance of maritime traffic using the network of over-the-horizon radars

Ova disertacija se bavi optimizacijom i prilagođenjem algoritama za praćenje ciljeva za potrebe ublažavanja uticaja štetnih refleksija na proces praćenja ciljeva kod izahorizontskih radara sa površinskim talasom (HFSW radari), upotrebljenih za osmatranje pomorskih ciljeva i konceptualizacijom i realizacijom sistema za integrisano pomorsko praćenje ciljeva baziranog na HFSWR mrežama.This dissertation deals with the optimization and adaptation of target tracking algorithms to mitigate the impact of harmful reflections on the target tracking process in over-the-horizon high frequency surface wave radar (HFSW radar), used to observe maritime targets and conceptualize and implement an integrated maritime surveillance system based on HFSWR networks..

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery

Monitoring marine use is essential to effective management but is extremely challenging, particularly where capacity and resources are limited. To overcome these limitations, satellite imagery has emerged as a promising tool for monitoring marine vessel activities that are difficult to observe through publicly available vessel-tracking data. However, the broader use of satellite imagery is hindered by the lack of a clear understanding of where and when it would bring novel information to existing vessel-tracking data. Here, we outline an analytical framework to (1) automatically detect marine vessels in optical satellite imagery using deep learning and (2) statistically contrast geospatial distributions of vessels with the vessel-tracking data. As a proof of concept, we applied our framework to the coastal regions of Peru, where vessels without the Automatic Information System (AIS) are prevalent. Quantifying differences in spatial information between disparate datasets—satellite imagery and vessel-tracking data—offers insight into the biases of each dataset and the potential for additional knowledge through data integration. Our study lays the foundation for understanding how satellite imagery can complement existing vessel-tracking data to improve marine oversight and due diligence

Near Real-time S-AIS: Recent Developments and Implementation Possibilities for Global Maritime Stakeholders

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry

ADVANCED REPRESENTATION LEARNING STRATEGIES FOR BIG DATA ANALYSIS

With the fast technological advancement in data storage and machine learning, big data analytics has become a core component of various practical applications ranging from industrial automation to medical diagnosis and from cyber-security to space exploration. Recent studies show that every day, more than 1.8 billion photos/images are posted on social media, and 720 thousand hours of videos are uploaded to YouTube. Thus, to handle this large amount of visual data efficiently, image/video classification, object detection/recognition, and segmentation tasks have gathered a lot of attention since the decade. Consequently, the researchers in this domain has proposed various feature extraction, feature learning, and feature encoding algorithms for improving the generalization performance of the aforesaid tasks. For example, the generalization performance of the image classification models mainly depends on the choice of data representation. These models aim at building comprehensive representation learning (RL) strategies to encode the relationship among the input and output attributes from the raw big data. Existing RL strategies can be divided into three general categories: statistic approaches (e.g. probabilistic-based analysis, and correlation-based measures), unsupervised learning (e.g., autoencoders), and supervised learning (e.g., deep convolutional neural network (DCNN)). Among these categories, the unsupervised and supervised learning strategies using artificial neural networks (ANNs) have been widely adopted. In this direction, several auxiliary ideas have been proposed over the past decade, to improve the learning capability of the ANNs. For instance, Moore-Penrose (MP) inverse is exploited to refine the parameters (weights and biases) of a trained network. However, the existing MP inverse-based RL methods have an important limitation. The representations learned through the MP inverse-based strategies suffer from loosely-connected feature coding, resulting into a poor representation of the objects having lack of discriminative power. To address this issue, this dissertation proposes a set of eight novel MP inverse-based RL algorithms. The first part of this dissertation from Chapter 4 to Chapter 7 is dedicated to proposing novel width-growth models based on subnet neural network (SNN) for representation learning and image classification. In this part, a novel feature learning algorithm, coined Wi-HSNN is proposed, followed by an improved batch-by-batch learning algorithm, called OS-HSNN. Then, two novel SNNs are introduced to detect extreme outliers for one-class classification (OCC). Finally, a semi-supervised SNN, named SS-HSNN is introduced to extend the strategy from the supervised learning domain to the semi-supervised learning domain. The second part of this thesis, subsuming Chapter 8 and Chapter 9, focuses on improving the performance of the existing multilayer neural networks through harnessing the MP inverse. Here, a novel weight optimization strategy is proposed to improve the performance of multilayer extreme learning machines (ELMs), where the MP inverse is used to feedback the classification imprecision information from the output layer to the hidden layers. Then, a novel fast retraining framework is proposed to enhance the efficiency of transfer learning of DCNNs. The effectiveness of the proposed subnet- and retraining-based algorithms have been evaluated on several widely used image classification datasets, such as ImageNet and Places-365. Furthermore, we validated the performance of the proposed strategies in some extended domains, such as ship-target detection, food image classification, camera model identification and misinformation identification. The experimental results illustrate the superiority of the proposed algorithms

COVID-19 Impact on Global Maritime Mobility

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of AIS receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: CNM of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62% and -13.77% for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger traffic. This study is unprecedented for the uniqueness and completeness of the employed dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet

Spatio-Temporal Deep Learning Approaches for Addressing Track Association Problem using Automatic Identification System (AIS) Data

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the Automatic Identification System, which serves as a communication medium between neighboring ships and the control center. While traditional methods have relied on sequential tracking and physics-based models, the emergence of deep learning has significantly transformed techniques typically used in trajectory prediction, clustering, and anomaly detection. This transformation is largely attributed to the deep learning algorithm’s capability to model complex nonlinear relationships while capturing both the spatial and temporal dynamics of ship movement. Capitalizing on this computational advantage, our study focuses on evaluating different deep learning architectures such as Multi Model Long Short-Term Memory (LSTM), 1D Convolutional-LSTM, and Temporal-Graph Convolutional Neural Networks— in addressing the problem of track association. The performance of these proposed models are compared against different deep learning algorithms specialized in track association tasks using several real-life AIS datasets

Physics Infused LSTM Network for Track Association Based on Marine Vessel Automatic Identification System Data

In marine surveillance, a crucial task is distinguishing between normal and abnormal vessel movements to timely identify potential threats. Subsequently, the vessels need to be monitored and tracked until necessary action can be taken. To achieve this, a track association problem is formulated where multiple vessels\u27 unlabeled geographic and motion parameters are associated with their true labels. These parameters are typically obtained from the Automatic Identification System (AIS) database, which enables real-time tracking of marine vessels equipped with AIS. The parameters are time-stamped and collected over a long period, and therefore, modeling the inherent temporal patterns in the data is crucial for successful track association. The problem is further complicated by infrequent data collection (time gap) and track overlaps. Traditionally, physics-based models and Kalman-filtering algorithms are used for tracking problems. However, the performance of Kalman filtering is limited in the presence of time-gap and overlapping tracks, while physics-based models are unable to model temporal patterns. To address these limitations, this work employs LSTM, a special neural network architecture, for marine vessel track association. LSTM is capable of modeling long-term temporal patterns and associating a data point with its true track. The performance of LSTM is investigated, and its strengths and limitations are identified. To further improve the performance of LSTM, an integration of the physics-based model and LSTM is proposed. The performance of the joint model is evaluated on multiple AIS datasets with varying characteristics. According to the findings, the physics-based model performs better when there is very little or no time gap in the dataset. However, when there are time gaps and multiple overlapping tracks, LSTM outperforms the physics-based model. Additionally, LSTM is more effective with larger datasets as it can learn the historical patterns of the features. Nevertheless, the joint model consistently outperforms the individual models by leveraging the strengths of both approaches. Given that the AIS dataset commonly provides a long stretch of historical information with frequent time gaps, the combined model should improve the accuracy of vessel tracking

China Near Seas Combat Capabilities

The capstone U.S. Defense Department study on the future operational environment declares, China\u27s rise represents the most significant single event on the international horizon since the collapse of the Cold War. Understanding and assessing changes in China\u27s traditionally defensive naval strategy, doctrine, and force structure are of obvious importance to the U.S. Navy (USN) and other Pacific navies concerned with the possible security implications of that rise. This chapter examines the development of the Chinese navy\u27s Houbei (Type 022) fast-attack-craft force and its roles and missions in China\u27s near seas and discusses implications for the U.S. Navy and other navies in the region.https://digital-commons.usnwc.edu/cmsi-red-books/1010/thumbnail.jp

Canadian Navy and domestic maritime enforcement

The objective of this research is to evaluate the employment of the Canadian Navy in a maritime enforcement role within the Canadian maritime zones. This investigation is comprised of two main parts: an analysis of the Canadian political and regulatory structures, as well as an analysis of naval enforcement operations. The marine geography of Atlantic Canada is described through six key ocean-use sectors, followed by an analysis of important oceans policy initiatives, and the federal government's ad hoc approach to security and defence policy formulation. The mandates, jurisdictions, and general capabilities of Canadian federal departments with either direct or indirect links to marine security and maritime enforcement are discussed, as well as the legal framework for the use of Canadian military forces for domestic operations. The second part of the thesis analyses the capabilities that the Navy brings to maritime security and enforcement operations. These include the contribution to maritime domain awareness, government "presence" derived through aerial surveillance, search and rescue operations, and naval support to fisheries enforcement. An analysis of patrol patterns is offered, as well as spatial analyses of at-sea inspection data. Two exploratory studies that address the perceived deterrent value of naval support to fisheries enforcement, and public opinion as it pertains to naval support to constabulary operations are presented, as well as the effect that fisheries support missions have on the combat readiness of warships. The thesis suggests that the Canadian Navy could take on a greater role in domestic enforcement, and a proposal is made for enhanced legal powers. The thesis ends by summarizing the Navy's important role championing and enabling improvement in the government's Marine Security Response System, as well as a whole-of- government approach to maritime surveillance planning.EThOS - Electronic Theses Online ServiceGBUnited Kingdo