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The problem of decision fusion in wireless sensor networks for distributed detection applications has mainly been considered in scenarios where sensor observations are conditionally independent and both local sensor statistics as well as wireless channel conditions are available for fusion rule design. In this paper, kernel-based learning algorithms for the design of decision fusion rules are presented...
Bayesian networks are often proposed as a method for high-level information fusion. However, a Bayesian network relies on strong assumptions about the underlying probabilities. In many cases it is not realistic to require such precise probability assessments. We show that there exists a significant set of problems where credal networks outperform Bayesian networks, thus enabling more dependable decision...
In theory, a good joint particle filter allows to approximate the exact Bayesian filter solution arbitrarily well. This has motivated a strong and successful development of single target tracking particle filters. Nevertheless, for tracking multiple closely spaced maneuvering targets, there is evidence in literature which seems to contradict the theoretical expectation. The mystery of this apparent...
In this paper we develop a subjective effects-based assessment method. This method takes subjective assessments regarding the activities of a plan as inputs. From these assessments and a cross impact matrix that represents the impact between all elements of the plan we calculate assessments for all other plan elements. The method is based on belief functions and their combination under a new generalization...
Mutual information (MI) has proven its effectiveness for automated multimodal image registration for numerous remote sensing applications like image fusion. We analyze MI performance with respect to joint histogram bin size and the employed joint histogramming technique. The affect of generalized partial volume estimation (GPVE) utilizing B-spline kernels with different histogram bin sizes on MI performance...
In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake. When the detections are fed to a tracking system like the Probabilistic Data Association Filter, the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even more severe in multitarget environments where...
This paper presents Monte Carlo (MC) methods for multi-target tracking and data association. We focus on comparing different estimation methods based on joint and non-joint state particle filters (PF) and joint probabilistic data association (JPDA) techniques. A novel data association algorithm for PF, founded on a combination of PDA and nearest neighbour (NN) techniques, is also developed. In this...
A formal approach to the design of situation analysis and decision support systems is justified and unavoidable if one is interested in reproducibility/traceability of results, satisfaction of constraints, and a language to represent and reason about dynamic situations. In this paper, we propose the integration of two multiagent modeling paradigms, abstract state machines and interpreted systems,...
Enemy Courses of Action (ECOAs) play a central role in the process of situation development in military decision-making. In order to reason about ECOAs, it would be necessary to adequately represent them in a formalism that allows for automatic reasoning. In this paper, we examine the benefits and drawbacks of representing ECOAs within several frameworks that have been encoded as OWL ontologies.
In this paper the problem of the simultaneous tracking of multiple video objects is addressed. In the proposed approach, each tracker behaves independently using corners and gradient-based information until an interaction with other trackers is reported. During the interaction, a new Bayesian method that allows the exploitation of the information of each tracker in a collaborative way is used. By...
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