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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...
Maritime surveillance of coastal regions requires the processing of data from a large number of heterogeneous surveillance sources. The generation of effective maritime domain awareness requires that the tracks from these sources must be fused. An automated fusion process that supports maritime domain awareness requires that the tracks from heterogeneous sources be modeled in such a way as to support...
The performance of a tracking filter can be evaluated in terms of the filterpsilas optimality conditions. Testing for optimality is necessary because the estimation error covariance as provided by the filter is not a reliable indicator of performance, which is known to be ldquooptimisticrdquo (inconsistent) particularly when there are model mismatches and target maneuvers. The conventional root-mean...
The paper presents two methods of updating the weights of a Gaussian mixture to account for the density propagation within a data assimilation setting. The evolution of the first two moments of the Gaussian components is given by the linearized model of the system. When observations are available, both the moments and the weights are updated to obtain a better approximation to the a posteriori probability...
The evaluation of a sentry system is dependent both on the performance of the implemented sensors and on the disposition of these sensors. In this paper, we are particularly interested in the global evaluation of the sentry system, taking into account the disposition of the sensors. Our approach is to evaluate the performance of an intruder in terms of detection probability. In order to evaluate the...
This paper presents the proactive logistics toolkit (PROLOG), a collaborative agent system and logistics network simulator that allows users to coordinate the transfer of supplies across a dynamically changing logistics network in a fully distributed, netcentric way. A collection of cognitive agents and asset classes models the logistics network: clients, their supply inventories, vehicles, and transportation...
Identification of tracked objects is a key capability of automated surveillance and information systems for air, surface and subsurface (maritime), and ground environments, improving situational awareness and offering decision support to operational users. The Bayesian-based identification data combining process (IDCP) provides an effective instrument for fusion of uncertain identity indications from...
Ensemble classifiers are known to generally perform better than each individual classifier of which they consist. One approach to classifier fusion is to apply Shaferpsilas theory of evidence. While most approaches have adopted Dempsterpsilas rule of combination, a multitude of combination rules have been proposed. A number of combination rules as well as two voting rules are compared when used in...
This paper presents a method for the simultaneous state and parameter estimation of finite-dimensional models of distributed systems monitored by a sensor network. In the first step, the distributed system is spatially and temporally decomposed leading to a linear finite-dimensional model in state space form. The main challenge is that the simultaneous state and parameter estimation of such systems...
We consider a network of sensors deployed to sense a spatio-temporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a state-space process that is perturbed by random noise and parametrized by an unknown parameter. To estimate the unknown parameter from the measurements that the sensors sequentially collect,...
State estimation and reconstruction quality of distributed phenomena that are monitored by a network of distributed sensors is strongly affected by communication failures, which is a problem in real-world sensor networks. In this paper, we propose a novel sensor scheduling approach named priority list sensor scheduling (PLSS). This approach facilitates efficient distributed estimation in sensor networks,...
Different information theoretic sensor management approaches are compared in a Bayesian target-tracking problem. Specifically, the performance using the expected Renyi divergence with different parameter values is compared theoretically and experimentally. Included is the special case in which the expected Renyi divergence is equal to the expected Kullback-Leibler divergence, which is also equivalent...
Fusion of observational data acquired by human observers and couched in linguistic form is a modern-day challenge for the fusion community. This paper describes a basic research effort examining various strategies for associating and exploiting such data for intelligence analysis purposes. An overall approach is described that involves Latent Semantic Analysis, Inexact Graph Matching, formal ontology...
Statistical mechanics has proven to be a useful model for drawing inferences about the collective behavior of individual objects that interact according to a known force law (which for a more general usage is referred to as interacting units.). Collective behavior is determined not by computing F = ma for each interacting unit because the problem is mathematically intractable. Instead, one computes...
We give a range of techniques to effectively apply on-line learning algorithms, such as Perceptron and Winnow, to both on-line and batch fusion problems. Our first technique is a new way to combine the predictions of multiple hypotheses. These hypotheses are selected from the many hypotheses that are generated in the course of on-line learning. Our second technique is to save old instances and use...
Principal ideas of the minC combination are recalled. A mathematical structure of generalized frames of discernment is formalized. A generalized schema for a computation of the minC combination is presented. Conflicting belief masses redistribution among non-conflicting focal elements is overviewed. Final general formulas for computation of the minC combination are presented. Some examples of computation...
In this paper, we establish a link between belief functions on real numbers and the maximal coherent sets obtained in the framework of possibilistic distributions. Having proposed an original disjunctive rule of combination in the framework of continuous belief functions, we demonstrate theoretically that maximal coherent sets can be viewed as a particular case in the framework of belief functions.
This paper presents a high level fusion approach suitable for automotive sensor networks with redundant field of views. The advantage of this method is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. The proposed algorithm deals with the issue of multidimensional assignment as the test vehicle comprises four forward looking...
Having a correct and timely classification solution for objects has become increasingly important as well as increasingly difficult to obtain in new maritime military missions; a decision support system is therefore needed. In decision support systems a challenge lies in how operator and system belief can be reconciled. This paper presents a support system for the classification process using dezert-smarandache...
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...
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