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The tutorial is an overview of tracking and data fusion for surveillance systems with applications both to defense and civilian systems. It is divided into four parts: Part 1 - Filtering: Covers the topics related to state estimation for stochastic dynamic systems: optimal Bayesian estimator, Kalman filter, nonlinear filters (extended and unscented Kalman filter, Gaussian sum filter, particle filter);...
Given a map of a polygon-shaped search area with obstacles and a group of mobile networked observers equipped with radiation dose counters (such as the Geiger-Muller counter), the data fusion problem is twofold: (1) to establish if any radioactive point sources are present in the area; (2) if present, to determine their number and their parameters (locations and intensities of radiation). The detection/estimation...
The paper develops an information fusion system that aims at supporting a commander's decision making by providing an assessment of threat, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled in the framework of the valuation-based system (VBS), by a network of entities and relationships between them. The...
Presented here is the theoretical basis of data fusion for the purpose of target identification using the belief function theory. The key feature is that we allow the knowledge sources to supply their information in the form of uncertain implication rules. How these rules can be elegantly handled within the framework of the belief function theory is described. A small scale, practical example for...
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