Michael L. Hinman

SAWA: an assistant for higher-level fusion and situation awareness

Situation awareness involves the identification and monitoring of relationships among level-one objects. This problem in general is intractable (i.e., there is a potentially infinite number of relations that could be tracked) and thus requires additional constraints and guidance defined by the user if there is to be any hope of creating practical situation awareness systems. This paper describes a Situation Awareness Assistant (SAWA) that facilitates the development of user-defined domain knowledge in the form of formal ontologies and rule sets and then permits the application of the domain knowledge to the monitoring of relevant relations as they occur in evolving situations. SAWA includes tools for developing ontologies in OWL and rules in SWRL and provides runtime components for collecting event data, storing and querying the data, monitoring relevant relations and viewing the results through a graphical user interface. An application of SAWA to a scenario from the domain of supply logistics is also presented.

Realizing situation awareness within a cyber environment

Situation Awareness (SA) problems all require an understanding of current activities, an ability to anticipate what may happen next, and techniques to analyze the threat or impact of current activities and predictions. These processes of SA are common regardless of the domain and can be applied to the detection of cyber attacks. This paper will describe the application of a SA framework to implementing Cyber SA, describe some metrics for measuring and evaluating systems implementing Cyber SA, and discuss ongoing work in this area. We conclude with some ideas for future activities.

Some computational approaches for situation assessment and impact assessment

This paper will provide an overview of several research efforts in the area of Information Fusion being conducted at the Fusion Technology Branch, Air Force Research Laboratory. It will describe a series of innovative approaches of traditional fusion algorithms and heuristic reasoning techniques to improve situational assessment and threat prediction. Approaches discussed include Bayesian techniques, knowledge based approaches, artificial neural systems (neural networks), fuzzy logic, and genetic algorithms.

Evaluating algorithmic techniques in supporting situation awareness

How well does an algorithm support its purpose and user base? Has automation provided the user with the ability to augment their production, quality or responsiveness? In a number of systems today these questions can be answered by either Measures of Performance (MOP) or Measures of Effectiveness (MOE). However, the fusion community has not yet developed sufficient measures and has only recently devoted a concerted effort to address this deficiency. In this paper, we will summarize work in metrics for the lower levels of fusion (object ID, tracking, etc) and discuss whether these same metrics still apply to the higher levels (Situation Awareness), or if other approaches are necessary. We conclude this paper with a set of future activities and direction.

A situation awareness model applied to multiple domains

Full Spectrum Dominance, or as defined by Joint Vision 2020, the ability to be persuasive in peace, decisive in war and preeminent in any form of conflict, cannot be accomplished without the ability to know what the adversary is currently doing as well as the capacity to correctly anticipate the adversarys future actions. A key component in the ability to predict the adversarys intention is Situation Awareness (SA). In this paper we provide a discussion of an SA model, examine a specific instantiation of the model and demonstrate how it has been applied to two specific domains: Global Monitoring and Cyber Awareness. We conclude this paper with a discussion on future work.

Lessons learned from developing SAWA: a situation awareness assistant

SAWA is a situation awareness assistant being developed by Versatile Information Systems, Inc. During the process of its development, several lessons were learned about advantages and limitations of certain approaches, techniques, and technologies as they are applied to situation awareness. This paper begins with an overview of SAWA and then focuses on some of the more significant lessons learned. These include the pros and cons of leveraging semantic Web technologies, the handling of time-varying attributes, and the processing of uncertainty.

Mobile agent-based distributed fusion (MADFUSION) system

This paper considers a system architecture referred to as the mobile agent-based distributed fusion (MADFUSION) system. The system environment consists of a peer-to-peer ad-hoc network in which information may be dynamically distributed and collected via publish/subscribe functionality implemented at each node of the network to facilitate data sharing and decision making in Level 2 fusion. The Level 2 decision making process implemented in the system consists of the enhanced doctrinal template matching (EDTM) algorithm which is shown to be an improvement over the pre-existing doctrinal template matching algorithm. This algorithm is developed to operates on information obtained from lower layer fusion processes in order to identify aggregated groups of entities. The template matching algorithm is shown to be an improvement over a previously existing algorithm. The MADFUSION system is proposed to extend the client/server architecture of various publish/subscribe applications to an architecture providing decentralization, re-configurability, mobility, attainability and prevention of single points of failure. The system is implemented in a wireless ad-hoc network (802.11b) and performs the publish/subscribe functionality through the implementation of a mobile agent based framework. The software agents travel deterministically from node-to-node carrying a data payload consisting of information which may be subscribed to by users within the network. Within this system, situation awareness (Level 2 fusion) can be sought by using these multi-domain information sources (GMTI, Video, or SAR) for evaluation at each node with different distributed information fusion algorithms.

Situation assessment and impact assessment activities in information fusion

This paper will provide a description of multiple research efforts in the area of information fusion being conducted at the Fusion Technology Branch, Air Force Research Laboratory. It will describe a series of innovative approaches of traditional fusion algorithms that heuristic reasoning techniques to improve situational assessment and threat prediction. Major features of the presentation will cover Bayesian techniques, Knowledge Based approaches, Artificial Neural Systems (Neural Networks), Fuzzy Logic, and Genetic Algorithms.

Final results on enhanced all source fusion (EASF)

The report will highlight the final results of an Advanced Technology Demonstration effort for an enhanced all source fusion system recently developed at the Fusion Technology Branch, Air Force Research Laboratory (IFEA). It will describe an innovative approach of traditional fusion algorithms and heuristic reasoning techniques to significantly improve the detection, identification, location and tracking of mobile red, blue and gray components of the electronic environment.

Object aggregation using Neyman-Pearson analysis

This paper presents a novel approach to: 1) distinguish military vehicle groups, and 2) identify names of military vehicle convoys in the level-2 fusion process. The data is generated from a generic Ground Moving Target Indication (GMTI) simulator that utilizes Matlab and Microsoft Access. This data is processed to identify the convoys and number of vehicles in the convoy, using the minimum timed distance variance (MTDV) measurement. Once the vehicle groups are formed, convoy association is done using hypothesis techniques based upon Neyman Pearson (NP) criterion. One characteristic of NP is the low error probability when a-priori information is unknown. The NP approach was demonstrated with this advantage over a Bayesian technique.