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In this paper we describe a particle filter algorithm that allows incorporation of prior knowledge about future states. Incorporation of such knowledge can significantly reduce the uncertainty in the estimation of future state predictions. Estimation of the state is based on a transition model where the current state is not only conditioned on the previous state but also on an attractive potential...
This paper evaluates a probabilistic approach to data association in a class of tracking problems characterised through intermittent, sparse observations. Examples are tracking of a specific target, such as a suspicious person or a car in urban environments. The used data stems from disparate, often simple detectors, each capable of detecting one type of a feature, such as a license plate, car type,...
This paper introduces a novel approach to robust tracking that combines Particle Filters (PFs) and estimation of physical constraints using Bayesian Networks (BNs). Heterogeneous Context Data (CD) describing the environment in which tracked objects move, is fused with the help of BNs. The resulting uncertain constraints are incorporated into the filtering process through a modification of the importance...
This paper presents a scenario-based approach to deal with uncertainties in situation assessment problems. Scenario representation is based on causal models, whereas scenario generation involves the estimation of the states of model variables, done by means of observations and inferences of hidden states by using domain knowledge. Moreover, scenario management is addressed by means of a probabilistic...
Rhino poaching in South Africa is leading to a catastrophic reduction in the rhino population. In this paper a Bayesian network causal model is proposed to model the underlying (causal) relationships that lead to rhino poaching events. The model may be used to fuse a collection of heterogeneous information sources. If a game reserve is partitioned into several geographical areas or cells, the model...
The paper evaluates a class of fusion systems that support interpretation of complex patterns consisting of large numbers of heterogeneous data obtained from distributed sources at different points in time. The fusion solutions in such domains must be able to process large quantities of heterogeneous information of different quality and adapt at runtime to accommodate for new data sources. This requires...
This paper discusses a novel approach to large scale information fusion in contemporary applications, such as environmental monitoring, crisis management, maritime security, etc. The emphasis is on tractable implementation of sound and reliable fusion systems which can cope with large quantities of heterogeneous information obtained via sensors, mobile communication devices, Internet and databases...
This paper discusses modeling solutions that support detection of gaseous chemical substances and source localization in applications that are characterized by large numbers of noisy information sources, absence of calibrated concentration measurements and lack of detailed knowledge about the physical processes. In particular, we introduce a solution based on discrete Bayesian networks which allows...
This paper introduces design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing constellations of information sources on the fly. The presented approach exploits the locality of relations in causal probabilistic processes, which facilitates decentralized modeling and information fusion. Observed events...
This paper is focusing on exact Bayesian reasoning in systems of agents, which represent weakly coupled processing modules supporting collaborative inference through message passing. By using the theory on factor graphs and cluster graphs we (i) analyze the suitability of the existing approaches to modular inference with respect to a relevant class of domains and (ii) derive methods for construction...
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