KEYWORDS: Information security, Computer security, Data processing, Control systems, Network security, Data modeling, Systems modeling, Receivers, Prototyping, Lithium
Battlefield monitoring involves collecting streaming data from different sources, transmitting the data over a
heterogeneous network, and processing queries in real-time in order to respond to events in a timely manner.
Nodes in these networks differ with respect to their trustworthiness, processing, storage, and communication
capabilities. Links in the network differ with respect to their communication bandwidth. The topology of the
network itself is subject to change, as the nodes and links may become unavailable. Continuous queries executed
in such environments must also meet some quality of service (QoS) requirements, such as, response time and
throughput. Data streams generated from the various nodes in the network belong to different security levels;
consequently, these must be processed in a secure manner without causing unauthorized leakage or modification.
Towards this end, we demonstrate how an existing complex event processing system can be extended to execute
queries and events in a secure manner in such a dynamic and heterogeneous environment.
Tracking process captures the state of an object. The state of an object is defined in terms of its dynamic and static
properties such as location, speed, color, temperature, size, etc. The set of dynamic and static properties for tracking very
much depends on the agency who wants to track. For example, police needs different set of properties to tracks people
than to track a vehicle than the air force. The tracking scenario also affects the selection of parameters. Tracking is done
by a system referred to in this paper as "Tracker." It is a system that consists of a set of input devices such as sensors and
a set of algorithms that process the data captured by these input devices. The process of tracking has three distinct steps
(a) object discovery, (b) identification of discovered object, and (c) object introduction to the input devices. In this paper
we focus mainly on the object discovery part with a brief discussion on introduction and identification parts. We
develops a formal tracking framework (model) called "Discover, Identify, and Introduce Model (DIIM)" for building
efficient tracking systems. Our approach is heuristic and uses reasoning leading to learning to develop a knowledge base
for object discovery. We also develop a tracker for the Air Force system called N-CET.
KEYWORDS: Environmental management, Logic, Knowledge management, Space operations, Data processing, Defense and security, Information fusion, Data modeling, Web services, Control systems
The Department of Defense is making significant investments to construct systems, built upon web services and their supporting technologies, that strive to achieve the goals of net-centricity. While these technologies address several of the traditional stumbling blocks to integration and interoperability, they leave issues of information management largely unaddressed. Indeed, the broad availability of these systems exacerbates, rather than reduces, stresses on our information management capabilities. This paper discusses the enterprise-level information management infrastructure objectives for providing net-centric military capabilities and more fundamental technical challenges derived from them.
This paper describes a novel technique to detect military convoy’s moving patterns using the Ground Moving Target Indicator (GMTI) data. The specific pattern studied here is the moving vehicle groups that are merging onto a prescribed location. The algorithm can be used to detect the military convoy’s identity so that the situation can be assessed to prevent hostile enemy military advancements. The technique uses the minimum error solution (MES) to predict the point of intersection of vehicle tracks. Comparing this point of intersection to the prescribed location it can be determined whether the vehicles are merging. Two tasks are performed to effectively determine the merged vehicle group patterns: 1) investigate necessary number of vehicles needed in the MES algorithms, and 2) analyze three decision rules for clustering the vehicle groups. The simulation has shown the accuracy (88.9% approx.) to detect the vehicle groups that merge at a prescribed location.
This paper describes a Publish and Subscribe capability developed under the Air Force Research Laboratory s (AFRL) Joint Battlespace Infosphere (JBI) project. The paper will give a brief description of the JBI and it s core service components of publish, subscribe and query. A detailed description fo the Pub/Sub system design and implementation will then be given describing how and where Java, Jini, and XML technologies were used to describe information objects,
match subscribers to appropriate dissemination nodes, and disseminate information objects to subscribing clients. Fianlly we describe a number of applications that are currently using the Pub/Sub capability.
This paper is concerned with the implementation of the SAR wavefront reconstruction algorithm on a high performance computer. For this purpose, the imaging algorithm is reformulated as a coherent processing (spectral combination) of images that are formed from a set of subapertures of the available synthetic aperture. This is achieved in conjunction with extracting the signature of a specific target region (digital spotlighting). Issues that are associated with implementing the algorithm on SMP-HPCs and DMP-HPCs are discussed. The results using the FOPEN P-3 SAR data are provided.
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