In this paper we present an application where we put together two methods for clustering and classification into a force aggregation method. Both methods are based on conflicts between elements. These methods work with different type of elements (intelligence reports, vehicles, military units) on different hierarchical levels using specific conflict assessment methods on each level. We use Dempster-Shafer theory for conflict calculation between elements, Dempster-Shafer clustering for clustering these elements, and templates for classification. The result of these processes is a complete force aggregation on all levels handled.

In this paper we develop a method for conflict management within Dempster-Shafer theory. The idea is that each piece of evidence is discounted in proportion to the degree that it contributes to the conflict. This way the contributors of conflict are managed on a case−by−case basis in relation to the problem they cause. Discounting is performed in a sequence of incremental steps, with conflict updated at each step, until the overall conflict is brought down exactly to a predefined acceptable level.

The paper discusses a recently developed demonstrator system where new ideas in tactical information fusion may be tested and demonstrated. The main services of the demonstrator are discussed, and essential experience from the use and development of the system is shared.

Abstract- In this paper we develop a particle filtering approach for grouping observations into an unspecified number of clusters. Each cluster corresponds to a potential target from which the observations originate. A potential clustering with a specified number of clusters is represented by an association hypothesis. Whenever a new report arrives, a posterior distribution over all hypotheses is iteratively calculated from a prior distribution, an update model and a likelihood function. The update model is based on an association probability for clusters given the probability of false detection and a derived probability of an unobserved target. The likelihood of each hypothesis is derived from a cost value of associating the current report with its corresponding cluster according to the hypothesis. A set of hypotheses is maintained by Monte Carlo sampling. In this case, the state-space, i.e., the space of all hypotheses, is discrete with a linearly growing dimensionality over time. To lower the complexity further, hypotheses are combined if their clusters are close to each other in the observation space. Finally, for each time-step, the posterior distribution is projected into a distribution over the number of clusters. Compared to earlier information theoretic approaches for finding the number of clusters this approach does not require a large number of trial clusterings, since it maintains an estimate of the number of clusters along with the cluster configuration.

Abstract – Recursive processing of Gaussian mixture functions inevitably leads to a large number of mixture components. In order to keep the computational complexity at a feasible level, the number of their components has to be reduced periodically. There already exists a variety of algorithms for this purpose, bottom-up and top-down approaches, methods that take the global structure of the mixture into account or that work locally and consider few mixture components at the same time. The mixture reduction algorithm presented in this paper can be catego-rized as global top-down approach. It takes a clustering algorithm originating from the field of theoretical computer science and adapts it for the problem of Gaussian mixture reduction. The achieved results are on the same scale as the results of the current “state-of-the-art ” algorithm PGMR, but, depending on the input size, the whole procedure performs significantly faster.

Abstract not found

We develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions into simple support and inverse simple support functions that are clustered based on their pairwise generalized weights of conflict, constrained by weights of attraction assigned to keep track of all decompositions. The generalized conflict c 2 ð 1; 1Þ and generalized weight of conflict J 2 ð 1; 1Þ are derived in the combination of simple support and inverse simple support functions.

In this paper we develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions into simple support and inverse simple support functions that are clustered based on their pairwise generalized weights of conflict, constrained by weights of attraction assigned to keep track of all decompositions. The generalized conflict c ∈ (−∞, ∞) and generalized weight of conflict J − ∈ (−∞, ∞) are derived in the combination of simple support and inverse simple support functions.

The Swedish Defence Research Agency has developed a concept demonstrator for demonstrating information fusion methodology focused on intelligence processing at the division level for a future Network Based Defence (NBF) / Network Centric Warfare (NCW) C4ISR system. The demonstrator integrates force aggregation, particle filtering and sensor allocation methods to construct, dynamically update and maintain a situation picture.

Abstract—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. Keywords—simulation, decision support, operational planning, effects-based planning, EBP, effects-based approach to operations, EBAO. I.