Fredrik Johansson

A Bayesian network approach to threat evaluation with application to an air defense scenario

In this paper, a precise description of the threat evaluation process is presented. This is followed by a review describing which parameters that have been suggested for threat evaluation in an air surveillance context throughout the literature, together with an overview of different algorithms for threat evaluation. Grounded in the findings from the literature review, a threat evaluation system have been developed. The system is based on a Bayesian network approach, making it possible to handle imperfect observations. The structure of the Bayesian network is described in detail. Finally, an analysis of the systempsilas performance as applied to a synthetic scenario is presented.

Detection of vessel anomalies - a Bayesian network approach

In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.

A Comparison between Two Approaches to Threat Evaluation in an Air Defense Scenario

Threat evaluation is a high-level information fusion problem of high importance within the military domain. This task is the foundation for weapons allocation, where assignment of blue force (own) weapon systems to red force (enemy) targets is performed. In this paper, we compare two fundamentally different approaches to threat evaluation: Bayesian networks and fuzzy inference rules. We conclude that there are pros and cons with both types of approaches, and that a hybrid of the two approaches seems both promising and viable for future research.

Implementation and integration of a Bayesian Network for prediction of tactical intention into a ground target simulator

Prediction of the enemys intention is a main issue of threat analysis, and, hence, will be an important part of the C2-systems of tomorrow. A technique that can be useful for this kind of predictions is Bayesian networks (BNs). We have developed a BN for prediction of the enemys tactical intention, and the implemented BN has been integrated into a ground target simulation framework. The general problem of how to find appropriate prior distributions for BNs has been addressed by developing a tool for data collection, which may make it easier to come up with appropriate prior distributions, by learning conditional probability tables from collected cases, i.e. parameter learning

Real-time allocation of defensive resources to rockets, artillery, and mortars

The protection of defended assets such as military bases and population centers against ballistic weapons (e.g. rockets and mortars) is a highly relevant problem in the military conflicts of today and tomorrow. In order to neutralize threats of this kind, they have to be detected and engaged before causing any damage to the defended assets. We propose algorithms for solving the resource allocation problem in real-time, and empirically investigate their performance using the open source testbed SWARD. The results show that a particle swarm optimization algorithm produce high quality solution for small-scale problems, and that a genetic algorithm yields the best solutions for the largest tested problem instances.

SWARD: System for weapon allocation research & development

The allocation of firing units to hostile targets is an important process within the air defense domain. Many algorithms have been proposed for solving various weapon allocation problems, but evaluation of the performance of such algorithms is problematic, since it does not exist any standard scenarios on which to test the algorithms. It is to a large extent unknown how weapon allocation algorithms compare to each other when it comes to solution quality. We have developed the testbed SWARD, making it possible to systematically compare algorithm performance, and to support the development of new weapon allocation algorithms.

Extending the scope of situation analysis

The use of technology to assist human decision making has been around for quite some time now. In the literature, models of both technological and human aspects of this support can be identified. However, we argue that there is a need for a unified model which synthesizes and extends existing models. In this paper, we give two perspectives on situation analysis: a technological perspective and a human perspective. These two perspectives are merged into a unified situation analysis model for semi-automatic, automatic and manual decision support (SAM)2. The unified model can be applied to decision support systems with any degree of automation. Moreover, an extension of the proposed model is developed which can be used for discussing important concepts such as common operational picture and common situation awareness.

A testbed based on survivability for comparing threat evaluation algorithms

Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the targets velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe an implemented testbed in which threat evaluation algorithms can be compared to each other, based on a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets). Our obtained results for two different threat evaluation algorithms are presented and analyzed.

Performance Evaluation of TEWA Systems for Improved Decision Support

In air defense situations, decision makers have to protect defended assets through assigning available firing units to threatening targets in real-time. To their help they have decision support systems known as threat evaluation and weapon allocation (TEWA) systems. The problem of performance evaluation of such systems is of great importance, due to their critical role. Despite this, research on this problem is close to non-existing. We are discussing the use of survivability and resource usage cost as comparative performance metrics, which can be used for comparing the effectiveness of different system configurations, by using simulations. These metrics have been implemented into a testbed, in which we have performed some comparative experiments. Our results show that changes of individual parts of the threat evaluation and weapon allocation system configuration can have a large effect on the effectiveness of the system as a whole, and illustrate how the metrics and the testbed can be used.