Effective Surveillance in the Waters of the Pitcairn Islands Marine Reserve

Located in the middle of the South Pacific Ocean, the Pitcairn Islands are a British Overseas Territory and, as of August 2016, home to one of the world's largest fully protected marine reserves. The Pitcairn Islands Marine Reserve, almost 3.5 times the size of the United Kingdom at about 830,000 square kilometres (320,465 square miles), serves as a habitat to at least 1,249 species of marine mammals, seabirds, and fish. It safeguards one of the most pristine ocean environments on Earth. But even for wealthy nations, enforcement of reserve rules—such as prohibitions on commercial fishing and seafloor mining—in such a remote area is challenging and expensive. To address that issue, new methods and cutting-edge technologies have been used to develop an enforcement strategy for this reserve

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti

Overview of contextual tracking approaches in information fusion

Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad

Enhanced cyberspace defense with real-time distributed systems using covert channel publish-subscribe broker pattern communications

In this thesis, we propose a novel cyberspace defense solution to the growing sophistication of threats facing networks within the Department of Defense. Current network defense strategies, including traditional intrusion detection and firewall-based perimeter defenses, are ineffective against increasingly sophisticated social engineering attacks such as spear-phishing which exploit individuals with targeted information. These asymmetric attacks are able to bypass current network defense technologies allowing adversaries extended and often unrestricted access to portions of the enterprise. Network defense strategies are hampered by solutions favoring network-centric designs which disregard the security requirements of the specific data and information on the networks. Our solution leverages specific technology characteristics from traditional network defense systems and real-time distributed systems using publish-subscribe broker patterns to form the foundation of a full-spectrum cyber operations capability. Building on this foundation, we present the addition of covert channel communications within the distributed systems framework to protect sensitive Command and Control and Battle Management messaging from adversary intercept and exploitation. Through this combined approach, DoD and Service network defense professionals will be able to meet sophisticated cyberspace threats head-on while simultaneously protecting the data and information critical to warfighting Commands, Services and Agencies.http://archive.org/details/enhancedcyberspa109454049US Air Force (USAF) author.Approved for public release; distribution is unlimited

Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment

Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet * that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model

U.S. Unmanned Aerial Vehicles (UAVS) and Network Centric Warfare (NCW) impacts on combat aviation tactics from Gulf War I through 2007 Iraq

Unmanned, aerial vehicles (UAVs) are an increasingly important element of many modern militaries. Their success on battlefields in Afghanistan, Iraq, and around the globe has driven demand for a variety of types of unmanned vehicles. Their proven value consists in low risk and low cost, and their capabilities include persistent surveillance, tactical and combat reconnaissance, resilience, and dynamic re-tasking. This research evaluates past, current, and possible future operating environments for several UAV platforms to survey the changing dynamics of combat-aviation tactics and make recommendations regarding UAV employment scenarios to the Turkish military. While UAVs have already established their importance in military operations, ongoing evaluations of UAV operating environments, capabilities, technologies, concepts, and organizational issues inform the development of future systems. To what extent will UAV capabilities increasingly define tomorrow's missions, requirements, and results in surveillance and combat tactics? Integrating UAVs and concepts of operations (CONOPS) on future battlefields is an emergent science. Managing a transition from manned- to unmanned and remotely piloted aviation platforms involves new technological complexity and new aviation personnel roles, especially for combat pilots. Managing a UAV military transformation involves cultural change, which can be measured in decades.http://archive.org/details/usunmannedaerial109454211Turkish Air Force authors.Approved for public release; distribution is unlimited

Surveillance technology and strategy for management of Portugal's EEZ

EEZ International, Summer 2003, p. 43-4

Current optical technologies for wireless access

The objective of this paper is to describe recent activities and investigations on free-space optics (FSO) or optical wireless and the excellent results achieved within SatNEx an EU-framework 6th programme and IC 0802 a COST action. In a first part, the FSO technology is briefly discussed. In a second part, we mention some performance evaluation criterions for the FSO. In third part, we briefly discuss some optical signal propagation experiments through the atmosphere by mentioning network architectures for FSO and then discuss the recent investigations in airborne and satellite application experiments for FSO. In part four, we mention some recent investigation results on modelling the FSO channel under fog conditions and atmospheric turbulence. Additionally, some recent major performance improvement results obtained by employing hybrid systems and using some specific modulation and coding schemes are presented

Are object detection assessment criteria ready for maritime computer vision?

Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision