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A crucial point in the decision-level identity fusion is to combine information in an appropriate way to generate an optimal decision, according to the individual information coming from a set of different sensors. An interesting approach was developed for the decision- level identity fusion, which use optimization techniques to minimize an objective function which measure the dissimilarities between...
Works have investigated the problem of the conflict redistribution in the fusion rules of evidence theories. As a consequence of these works, many new rules have been proposed. Now, there is not a clear theoretical criterion for a choice of a rule instead another. The present paper proposes a new theoretically grounded rule, based on a new concept of sensor independence. This new rule avoids the conflict...
Sensor resource management (or process refinement) is a element of any information fusion system. Common level 4 sensor management (SM) inter-relations to level 1 target tracking and identification have been developed in the literature. During Fusion06, a panel discussion was held to explore the challenges and issues pertaining to the interaction between SM and situation and threat assessment. This...
This paper is concerned with tracking of ground targets on roads and investigates possible ways to improve target state estimation via fusing a target's track with information about a road along which the target is believed to be traveling. A target track is estimated by a surveillance radar whereas a digital map provides the road network of a region under surveillance. When the information about...
The main challenge of future phased array multifunction radar will be to optimise the radar time budget to avoid deleterious overload effects. For this purpose, we propose a new technique for priority assignment of Search Domains. Then, we have developed new algorithms for most threatening trajectories computation based on Calculus of Variations approach developed for "shortest path computation"...
In nonlinear Bayesian estimation it is generally inevitable to incorporate approximate descriptions of the exact estimation algorithm. There are two possible ways to involve approximations: Approximating the nonlinear stochastic system model or approximating the prior probability density function. The key idea of the introduced novel estimator called Hybrid Density Filter relies on approximating the...
The ability to meaningfully assess performance is crucial for understanding, developing and comparing estimators. The optimality of an estimator relies on estimation criterion and there exists a significant gap between estimation criterion and application requirements, so the estimation criterion is not good for evaluating or comparing algorithms. Different viewpoints for performance comparison can...
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