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Works have investigated the problem of the conflict redistribution in the fusion rules of evidence theories. As a consequence of these works, many new rules have been proposed. Now, there is not a clear theoretical criterion for a choice of a rule instead another. The present paper proposes a new theoretically grounded rule, based on a new concept of sensor independence. This new rule avoids the conflict...
This paper presents a novel sequential variational inference algorithm for distributed multi-sensor tracking and fusion. The algorithm is based on a multi-sensor target representation where a target is represented jointly by its states at different sensors and a global state fusing all sensor data. A tree-structured graphical model is adopted to model the dependencies between these states at a time...
The formalization of network centric warfare (NCW) signaled a new Department of Defense drive towards systems that could conceivably interact towards the accomplishment of a common goal. But the debate regarding NCW possibilities and practicalities in terms of existing and potential technologies highlight that much work is needed before the theortical NCW can be realized. The Information and Cyberwarfare...
Bayesian networks (BNs) represent joint space probabilities compactly and enable one to carry out efficient inferencing. Although the Dempster-Shafer (DS) belief theoretic framework captures a wider class of imperfections, its utility in such graphical models is limited. This is mainly due to the requirement of having to maintain a basic probability assignment (BPA) for the whole power set of propositions...
In this paper we present an application that utilizes a novel two-level fusion architecture to detect and track disease outbreaks across public health system databases. In the first fusion level, collected data is used to detect and track indicative bio-events using latent semantic analysis and unsupervised clustering. In the second fusion level, clusters produced via the first are used to feed dynamic...
Bayesian networks are useful for predicting future activities on the battlefield. Bayesian mathematics provides the most benefit in JDL fusion levels 2+, i.e. situation, threat, and performance assessment. However, these networks are exceedingly difficult for the average person to develop, much less a soldier in the middle of a war. We are in the process of developing a Bayesian modeling aid that...
Evidence gathered from different sources may have different reliabilities. Such reliability should be integrated into corresponding evidence model to make the evidence combination result rational. In this paper, a novel discounting strategy is developed for the integration of evidence's model and reliability. Dissimilar to the current one based on BPA, this strategy discounts the evidence's plausibility...
The traditional message passing algorithm developed by Pearl in 1980s provides exact inference for discrete poly-tree Bayesian networks. When there are multiple paths (loops) in the network, we can still apply Pearl's algorithm to provide approximate solutions and it is so-called "loopy propagation". However, when mixed random variables (continuous and discrete variables) are present in...
This paper details and deepens a previous work where the Interpreted Systems semantics was proposed as a general framework for situation analysis (SA). This framework is particularly efficient for representing and reasoning about knowledge and uncertainty when performing situation analysis tasks. Our approach of SA is to base our analysis on the production of state transition systems consisting in...
In this paper we show that causal probabilistic models can facilitate the design of robust and flexible fusion systems. Observed events resulting from stochastic causal processes can be modeled with the help of causal Bayesian networks, mathematically rigorous and compact probabilistic causal models. Bayesian networks explicitly represent conditional independence which facilitates decentralized modeling...
A method is proposed for converting a novelty measure such as produced by one-class SVMs or Kernel principal component analysis (KPCA) into a belief function on a well- defined frame of discernment. This makes it possible to combine one-class classification or novelty detection methods with other information expressed in the same framework such as expert opinions or multi-class classifiers.
Recently, several approaches have been proposed to merge possibly contradictory belief bases. This paper focuses on max-based merging operators applied to incommensurable ranked belief bases. We first propose a characterization of a result of merging using Pareto-like ordering on a set of possible solutions. Then we propose two equivalent ways to recover the result of merging. The first one is based...
The construction of belief networks is a widely used methodology for high level fusion modeling. While some of the components of a belief network deal with ambiguous (probabilistic) data, others may deal with vague (possibilistic) data. Given the need to represent both probabilistic and possibilistic components in a single belief network, a framework and toolset for building Hybrid networks, utilizing...
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....
This paper deals with enriched qualitative belief functions for reasoning under uncertainty and for combining information expressed in natural language through linguistic labels. In this work, two possible enrichments (quantitative and/or qualitative) of linguistic labels are considered and operators (addition, multiplication, division, etc) for dealing with them are proposed and explained. We denote...
This paper considers the accuracy of state estimation based on classification using Bayesian networks. It presents a method to localize network fragments that (i) are in a particular (rare) case responsible for a potential misclassification, or (ii) contain modeling errors that consistently cause misclassifications, even in common cases. We derive an algorithm that, within such fragments, can localize...
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