Pontus Hörling

An information fusion demonstrator for tactical intelligence processing in network-based defense

The Swedish Defence Research Agency (FOI) has developed a concept demonstrator called the Information Fusion Demonstrator 2003 (IFD03) for demonstrating information fusion methodology suitable for a future Network Based Defense (NBD) C4ISR system. The focus of the demonstrator is on real-time tactical intelligence processing at the division level in a ground warfare scenario. The demonstrator integrates novel force aggregation, particle filtering, and sensor allocation methods to create, dynamically update, and maintain components of a tactical situation picture. This is achieved by fusing physically modelled and numerically simulated sensor reports from several different sensor types with realistic a priori information sampled from both a high-resolution terrain model and an enemy organizational and behavioral model. This represents a key step toward the goal of creating in real time a dynamic, high fidelity representation of a moving battalion-sized organization, based on sensor data as well as a priori intelligence and terrain information, employing fusion, tracking, aggregation, and resource allocation methods all built on well-founded theories of uncertainty. The motives behind this project, the fusion methods developed for the system, as well as its scenario model and simulator architecture are described. The main services of the demonstrator are discussed and early experience from using the system is shared.

Using the impact matrix for predictive situational awareness

In order to manage situations efficiently, commanders need to be aware of possible future events that might occur. They also need to be aware of the relative probabilities of different events, so that they know which events to take into account when making plans of their own. In this paper, we describe a concept prototype that was developed at FOI during 2006 that helps commanders do these tasks. The impact matrix is a tool that has been used in business for risk handling. We describe the impact matrix and how it can be adapted for military use. To connect observations from soldiers and sensors to events, indicators are used as tags. Belief networks are used to connect indicators to events. Results from a preliminary experiment using a scenario based on an asymmetric conflict where a Swedish battle group is tasked with preserving peace are presented.

Simulation-based decision support for effects-based planning

In this paper we describe decision support and simulation techniques to facilitate effects-based planning. By using a decision support tool, a decision maker is able to test a number of feasible plans against possible courses of events and decide which of those plans is capable of achieving the desired military end state. The purpose is to evaluate plans and understand their consequences through simulating the events and producing outcomes which result from making alternative decisions. Plans are described in the effects-based approach to operations concept as a set of effects and activities that together will lead to a desired military end state. For each activity we may have several different alternatives. Together they make up all alternative plans, as an activity tree that may be simulated. Simulated plans that are similar in both their structure and consequence are clustered together by a Potts spin neural clustering method. These plans make up a robust set of similar plans that function as ready alternatives should dynamic replanning be necessary as the situation evolves.

Building an information fusion demonstrator

The paper describes an ongoing effort to build a demonstrator system where new ideas in information fusion may be tested and demonstrated. The motives behind this project, its system architecture and development process, and some of the fusion methods being developed for the system are briefly described.

Adapting a commercial simulation framework to the needs of information fusion research

The paper develops and argues usage requirements emanating from information fusion research on simulation frameworks. Based on these requirements, a next-generation simulation framework is currently being designed by one framework vendor.

Detecting Emergent Conflicts through Web Mining and Visualization

An ocean of data is available on the web. From this ocean of data, information can in theory be extracted and used by analysts for detecting emergent trends (trend spotting). However, to do this manually is a daunting and nearly impossible task. We describe a semi-automatic system in which data is automatically collected from selected sources, and to which linguistic analysis is applied to extract e.g., entities and events. After combining the extracted information with human intelligence reports, the results are visualized to the user of the system who can interact with it in order to obtain a better awareness of historic as well as emergent trends. A prototype of the proposed system has been implemented and some initial results are presented in the paper.

A Comparative Study between Using A * -search, Latin Hypercube and Genetic Algorithms in Design of Experiment for Simulation of Military Operational Plans *

In this paper we compare three alternative ways of designing a data farming experiment for a military operational planning problem. In this multi-objective optimization problem the goal is to minimize two different measures of effectiveness. Military plans under consideration are evaluated by an event based simulation system. We compare three different approaches for selecting which plans should be simulated; A*-search, genetic algorithms and Latin Hypercube. The more robust Latin Hypercube approach is found to be the better approach for this application compared to the other two more focused approaches.

Decision support for simulation-based operation planning

In this paper, we develop methods for analyzing large amounts of data from a military ground combat simulation system. Through a series of processes, we focus the big data set on situations that correspond to important questions and show advantageous outcomes. The result is a decision support methodology that provides commanders with results that answer specific questions of interest, such as what the consequences for the Blue side are in various Red scenarios or what a particular Blue force can withstand. This approach is a step toward taking the traditional data farming methodology from its analytical view into a prescriptive operation planning context and a decision making mode.