Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Applying the data fusion technique to blog opinion retrieval
Expert Systems with Applications: An International Journal
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
The weighted Condorcet fusion in information retrieval
Information Processing and Management: an International Journal
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When performing data fusion, one often measures where targets were and then wishes to deduce where targets currently are. There has been recent research on the processing of such out-of-sequence data. This research has culminated in the development of a number of algorithms for solving the associated tracking problem. This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together. The emphasis is not on advocating one approach over another on the basis of computational expense, but rather on understanding the relationships among the algorithms so that any approximations made are explicit. Results for a multi-sensor scenario involving out-of-sequence data association are used to illustrate the utility of this approach in a specific context.