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Reports generated by soldiers are common in time-critical military environments. Data fusion systems that attempt to process those reports must maintain the context for each set of observations to avoid inaccurate state estimates. This paper analyzes the selection and assignment of topical context under a Bayesian methodology. We present several techniques to decrease the hypothesis space and heuristics...
We consider a pair of correlated processes in {Zn}n=-infininfin and {Zn}n=-infininfin, where the former is observable and the latter is hidden. The uncertainty in the estimation of Sn upon the finite past history of Z0n-infin1 is H(Sn|)Z0ninfin1 which is a sequence of n. The limit of Cesaro mean of this sequence is called the estimation entropy. We show that the estimation entropy is the long...
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...
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...
In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature...
This paper presents algorithms for consistent joint localisation and tracking of multiple targets in wireless sensor networks under the decentralised data fusion (DDF) paradigm where particle representations of the state posteriors are communicated. This work differs from previous work as more generalised methods have been developed to account for correlated estimation errors that arise due to common...
We present local Bayesian fusion approaches for the reduction of storage and computational costs of Bayesian fusion which is detached from fixed modelling assumptions. Using local approaches, Bayesian fusion is not performed in detail on the whole space that is spanned by the quantities of interest but only locally - at least in regions that are task relevant with a high probability. These regions...
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