Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

AIS message extraction from overlapped AIS signals for SAT-AIS applications

The AIS (Automatic Identification System) is a communication standard for ships traveling the seas and oceans. It serves as a collision avoidance system by identifying nearby ships, thus assisting in safe navigation. The SAT-AIS (Satellite based Automatic Identification System) is a communication technology for ship traffic surveillance from space and is under active research and development worldwide. The basic principle of the SAT-AIS system is to monitor AIS channels. The motivation for using terrestrial AIS technologies with space applications is of great interest to safety organizations that monitor ship traffic in high seas and oceans. These regions far away from coastal zones are unreachable from the terrestrial antennas, which have a usual range of 40 kilometres. Successful application of the SAT-AIS could provide AIS data to coast guards and other agencies, with an hourly ship location update from every place on the planet. The first trials of SAT-AIS in 2006 suffered from some serious difficulties. As AIS was initially designed to be a terrestrial traffic avoidance application for ships, with the traffic participants communicating among their neighbours and the nearby coast guard, it was developed without resistivity against effects which arise when applied for space applications. Apart from signal strength and Doppler shift effects, which could be constructively handled, the demodulation of overlapped AIS messages proved to be a great challenge. This work analyses the problem of overlapping AIS signals and proposes innovative approaches for reconstructing these based on L^2 norm orthogonalization and projections. Moreover, the work showcases results of demodulation efficiency analysis for simulated real world application of satellite passes over a dedicated shipping region based on AIS channel simulation in noisy environment For more reliable AIS data reception in space, new dedicated frequencies are allocated for channels AIS3 and AIS4, which are being affirmed for all AIS transceiver installations from 2013. These new frequency channels carry dedicated messages with a ship position report, encapsulated into smaller data packets at lower report rates, which promises to partly eliminate the packet overlapping problem. Since the new Space-AIS format does not completely solve the packet collision problem and as the steady growth of interest on terrestrial-AIS message content received from space continues to persist, the topic of solving overlapped AIS signals remains vital for SAT-AIS applications

Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets

In the modern information age, the quantity and complexity of spatiotemporal data is increasing both rapidly and continuously. Sensor systems with multiple feeds that gather multidimensional spatiotemporal data will result in information clusters and overload, as well as a high cognitive load for users of these systems. To meet future safety-critical situations and enhance time-critical decision-making missions in dynamic environments, and to support the easy and effective managing, browsing, and searching of spatiotemporal data in a dynamic environment, we propose an asynchronous, scalable, and comprehensive spatiotemporal data organization, display, and interaction method that allows operators to navigate through spatiotemporal information rather than through the environments being examined, and to maintain all necessary global and local situation awareness. To empirically prove the viability of our approach, we developed the Event-Lens system, which generates asynchronous prioritized images to provide the operator with a manageable, comprehensive view of the information that is collected by multiple sensors. The user study and interaction mode experiments were designed and conducted. The Event-Lens system was discovered to have a consistent advantage in multiple moving-target marking-task performance measures. It was also found that participants’ attentional control, spatial ability, and action video gaming experience affected their overall performance

Ship target recognition

Includes bibliographical references.In this report the classification of ship targets using a low resolution radar system is investigated. The thesis can be divided into two major parts. The first part summarizes research into the applications of neural networks to the low resolution non-cooperative ship target recognition problem. Three very different neural architectures are investigated and compared, namely; the Feedforward Network with Back-propagation, Kohonen's Supervised Learning Vector Quantization Network, and Simpson's Fuzzy Min-Max neural network. In all cases, pre-processing in the form of the Fourier-Modified Discrete Mellin Transform is used as a means of extracting feature vectors which are insensitive to the aspect angle of the radar. Classification tests are based on both simulated and real data. Classification accuracies of up to 93 are reported. The second part is of a purely investigative nature, and summarizes a body of research aimed at exploring new ground. The crux of this work is centered on the proposal to use synthetic range profiling in order to achieve a much higher range resolution (and hence better classification accuracies). Included in this work is a comprehensive investigation into the use of super-resolution and noise reducing eigendecomposition techniques. Algorithms investigated include the Principal Eigenvector Method, the Total Least Squares Method, and the MUSIC method. A final proposal for future research and development concerns the use of time domain averaging to improve the classification performance of the radar system. The use of an iterative correlation algorithm is investigated

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective

Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Autonomous Monitoring of Contaminants in Fluids

The litigation and mitigation of maritime incidents suffer from a lack of information, first at the incident location, then throughout the evolution of contaminants such as spilled oil through the surrounding environment. Prior work addresses this through ocean and oil models, model directed sensor guidance and other observation methods such as satellites. However, each of these approaches and research fields have short-comings when viewed in the context of fast-response to an incident, and of constructing an all-in-one framework for monitoring contaminants using autonomous mobile sensors. In summary, models often lack consideration of data-assimilation or sensor guidance requirements, sensor guidance is specific to source locating, oil mapping, or fluid measuring and not all three, and data assimilation methods can have stringent requirements on model structure or computation time that may not be feasible. This thesis presents a model-based adaptive monitoring framework for the estimation of oil spills using mobile sensors. In the first of a four-stage process, simulation of a combined ocean, wind and oil model provides a state trajectory over a finite time horizon, used in the second stage to solve an adjoint optimisation problem for sensing locations. In the third stage, a reduced-order model is identified from the state trajectory, utilised alongside measurements to produce smoothed state estimates in the fourth stage, which update and re-initialise the first-stage simulation. In the second stage, sensors are directed to optimal sensing locations via the solution of a Partial Differential Equation (PDE) constrained optimisation problem. This problem formulation represents a key contributory idea, utilising the definition of spill uncertainty as a scalar PDE to be minimised subject to sensor, ocean, wind and oil constraints. Spill uncertainty is a function of uncertainty in (i) the bespoke model of the ocean, wind and oil spill, (ii) the reduced order model identified from sensor data, and (iii) the data assimilation method employed to estimate the states of the environment and spill. The uncertainty minimisation is spatio-temporally weighted by a function of spill probability and information utility, prioritising critical measurements. In the penultimate chapter, numerical case-studies spanning a 2500 km2 coastal area are presented. Here the monitoring framework is compared to an industry standard method in three scenarios: A spill monitoring and prediction problem, a retrodiction and monitoring problem and a source locating problem