Girt by sea: understanding Australia’s maritime domains in a networked world

This study aims to provide the background, language and context necessary for an informed understanding of the challenges and dilemmas faced by those responsible for the efficacy of Australia’s maritime domain awareness system. Abstract Against a rapidly changing region dominated by the rise of China, India and, closer to home, Indonesia, Australia’s approaches to understanding its maritime domains will be influenced by strategic factors and diplomatic judgements as well as operational imperatives.  Australia’s alliance relationship with the United States and its relationships with regional neighbours may be expected to have a profound impact on the strength of the information sharing and interoperability regimes on which so much of Australia’s maritime domain awareness depends. The purpose of this paper is twofold.  First, it seeks to explain in plain English some of the principles, concepts and terms that maritime domain awareness practitioners grapple with on a daily basis.  Second, it points to a series of challenges that governments face in deciding how to spend scarce tax dollars to deliver a maritime domain awareness system that is necessary and sufficient for the protection and promotion of Australia’s national interests

Future internet enablers for VGI applications

This paper presents the authors experiences with the development of mobile Volunteered Geographic Information (VGI) applications in the context of the ENVIROFI project and Future Internet Public Private Partnership (FI-PPP) FP7 research programme.FI-PPP has an ambitious goal of developing a set of Generic FI Enablers (GEs) - software and hardware tools that will simplify development of thematic future internet applications. Our role in the programme was to provide requirements and assess the usability of the GEs from the point of view of the environmental usage area, In addition, we specified and developed three proof of concept implementations of environmental FI applications, and a set of specific environmental enablers (SEs) complementing the functionality offered by GEs. Rather than trying to rebuild the whole infrastructure of the Environmental Information Space (EIS), we concentrated on two aspects: (1) how to assure the existing and future EIS services and applications can be integrated and reused in FI context; and (2) how to profit from the GEs in future environmental applications.This paper concentrates on the GEs and SEs which were used in two of the ENVIROFI pilots which are representative for the emerging class of Volunteered Geographic Information (VGI) use-cases: one of them is pertinent to biodiversity and another to influence of weather and airborne pollution on users’ wellbeing. In VGI applications, the EIS and SensorWeb overlap with the Social web and potentially huge amounts of information from mobile citizens needs to be assessed and fused with the observations from official sources. On the whole, the authors are confident that the FI-PPP programme will greatly influence the EIS, but the paper also warns of the shortcomings in the current GE implementations and provides recommendations for further developments

Innovative observing strategy and orbit determination for Low Earth Orbit Space Debris

We present the results of a large scale simulation, reproducing the behavior of a data center for the build-up and maintenance of a complete catalog of space debris in the upper part of the low Earth orbits region (LEO). The purpose is to determine the performances of a network of advanced optical sensors, through the use of the newest orbit determination algorithms developed by the Department of Mathematics of Pisa (DM). Such a network has been proposed to ESA in the Space Situational Awareness (SSA) framework by Carlo Gavazzi Space SpA (CGS), Istituto Nazionale di Astrofisica (INAF), DM, and Istituto di Scienza e Tecnologie dell'Informazione (ISTI-CNR). The conclusion is that it is possible to use a network of optical sensors to build up a catalog containing more than 98% of the objects with perigee height between 1100 and 2000 km, which would be observable by a reference radar system selected as comparison. It is also possible to maintain such a catalog within the accuracy requirements motivated by collision avoidance, and to detect catastrophic fragmentation events. However, such results depend upon specific assumptions on the sensor and on the software technologies

Sensor tasking utilizing deep reinforcement learning in a random finite set framework

There is a growing need to increase the capabilities of existing sensor arrays to monitor a large amount of space objects orbiting the Earth with a limited number of opportunities to observe these objects. Due to geopolitical considerations and financial cost, it is infeasible to create an array of sensors that can monitor each space object and accurately describe its state. Instead of brute force techniques by increasing the number of sensors worldwide, the current advancements in computational capability along with new algorithms for multi-target filtering and reinforcement learning has allowed a pathway to begin solving the non-myopic, heterogenous sensor tasking problem. This work employs the labeled multi-Bernoulli filter in conjunction with advanced, deep reinforcement learning techniques such as the policy gradient Q-learning algorithm and deep Q-networks. The filter and reinforcement learning techniqures are used together to track ten targets in geosynchronous orbit, while a linear Kalman filter and the reinforcement learning techniques are used to evaluate their effectiveness in multi-agent learning scenarios. The future deployment of these algorithms and their specific logistical considerations are also discussed with potential solutions.Aerospace Engineerin

Search-Based vs. Task-Based Space Surveillance for Ground-Based Telescopes

Persistent Space Situational Awareness (SSA) is one of the top priorities of the DoD. Currently the Space Surveillance Network (SSN) operates using only a task-based method. The goal of this thesis was to compare the current task-based space surveillance performance to a search-based method of space surveillance in the GEO belt region. The performance of a ground telescope network, similar to the Ground-Based Electro-Optical Deep Space Surveillance (GEODSS) network, was modeled and simulated using AGI’s Systems Tool Kit (STK) and Python. The model compared search-based and task-based space surveillance methods by simulating 813 Resident Space Objects (RSOs) on the summer solstice, fall equinox and winter solstice. Four performance metrics for comparing the search-based and task-based methods were minimum detectable size, detection rate, coverage area, and latency. The search-based method modeled six different search patterns at varying starting positions. Results show that the minimum detectable size average for task-based was 47.6 cm in diameter while search-based methods ranged from 38.3 cm - 45.4 cm in diameter. Detection rate for task-based was 100% while the search-based ranged from 91.7% - 96.8%. Coverage area for task-based was 46% of the GEO belt and the search-based method ranged from 3.5% - 84.4%. Average latency (revisit time) for task-based was 78 minutes and search-based methods ranged from 62 - 469 minutes. It was found that task-based surveillance was the better method for current operational conditions by using a weighted decision criteria. However, as the number of RSOs increase there is a point at which the search method has better performance

Low Earth Orbit Satellite Tracking Telescope Network: Collaborative Optical Tracking for Enhanced Space Situational Awareness

The Air Force Institute of Technology has spent the last seven years conducting research on orbit identification and object characterization of space objects through the use of commercial-off-the-shelf hardware systems controlled via custom software routines, referred to simply as TeleTrak. Year after year, depending on the research objectives, students have added or modified the system\u27s hardware and software to achieve their individual research objectives. In the last year, due to operating system and software upgrades, TeleTrak became inoperable. Furthermore, due to a lack of student overlap, knowledge of the basic operation of the TeleTrak deteriorated. This research re-establishes the basic understanding of the TeleTrak System and develops a plan to improve the telescope tracking controller performance. This research uses a subset of the SE process via the operational and system views to understand the tracking subsystem and develop timing tests to observe delays that could impact tracking. Basic tests revalidate and improve understanding of how the Meade telescopes interface with MATLAB. Calibration camera parameters are then refined, allowing a new technique for calibrating existing control algorithms. The analyses of the findings demonstrate that it is possible to improve the tracking controller, but it also uncovers previously undocumented issues with the Meade telescope mount. Future students interested in continuing this research, regardless of which telescope mount is used with TeleTrak, will benefit from the findings of this research

Dynamic sensor tasking and IMM EKF estimation for tracking impulsively maneuvering satellites

In order to efficiently maintain space situational awareness, care must be taken to optimally allocate expensive observation resources. In most situations the available sensors capable of tracking spacecraft have their time split between many different monitoring responsibilities. Tracking maneuvering spacecraft can be especially difficult as the schedule of maneuvers may not be known and will often throw off previous orbital models. Effectively solving this tasking problem is an ongoing focus of research in the area of space situational awareness. Most methods of automated tasking do not make use of interacting multiple model extended Kalman filter techniques to better track satellites during maneuvers. This paper proposes a modification to a Fisher information gain and estimated state covariance based sensor tasking method to take maneuver probability and multiple model dynamics into account. By incorporating the probabilistic maneuvering model, sensor tasking can be improved during satellite maneuvers using constrained resources. The proposed methods are verified through the use of numerical simulations with multiple maneuvering satellites and both orbital and ground-based sensors

Army Decade in Space

In the twelve short years since the announcement of the SMDC-ONE satellite initiative by Lieutenant General Kevin Campbell, then Commanding General of U.S. Army Space and Missile Defense Command (SMDC), SMDC has put in place an active program of satellite technology development and a Low Earth Orbit Investment Strategy that holds great promise for providing low-cost, responsive data from space as the next major evolution in technology to enable Multi-Domain Operations for the Army of 2028 and beyond. The first fruits of that initiative were seen ten years ago with launch and successful mission of the first SMDC-ONE satellite. This small satellite strategy has gained traction with Army and DoD leadership who embrace the small satellite paradigm. This paper discusses Army progress and lessons learned in the past ten years of small satellite efforts, discusses relationships with other organizations and looks forward to potential capabilities enabled by technology advancements and innovative partnerships

Conversational Sensing

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it possible to represent information fusion and situational awareness as a conversational process among actors - human and machine agents - at or near the tactical edges of a network. Motivated by use cases in the domain of security, policing and emergency response, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled natural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a flow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both trained and untrained sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by management and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects

Secure, Autonomous, Intelligent Controller for Integrating Distributed Emergency Response Satellite Operations

This report describes a Secure, Autonomous, and Intelligent Controller for Integrating Distributed Emergency Response Satellite Operations. It includes a description of current improvements to existing Virtual Mission Operations Center technology being used by US Department of Defense and originally developed under NASA funding. The report also highlights a technology demonstration performed in partnership with the United States Geological Service for Earth Resources Observation and Science using DigitalGlobe(Registered TradeMark) satellites to obtain space-based sensor data