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This paper defines a new concept and framework for constructing fusion rules for evidences. This framework is based on a referee function, which does a decisional arbitrament conditionally to basic decisions provided by the several sources of information. A simple sampling method is derived from this framework. The purpose of this sampling approach is to avoid the combinatorics which are inherent...
The evaluation of a sentry system is dependent both on the performance of the implemented sensors and on the disposition of these sensors. In this paper, we are particularly interested in the global evaluation of the sentry system, taking into account the disposition of the sensors. Our approach is to evaluate the performance of an intruder in terms of detection probability. In order to evaluate the...
Despite their importance in the search theory field, the two problems we introduce in this paper have not been widely studied in the literature. The first problem addresses the optimization of the search for multiple moving targets. The second one addresses the optimization of a cross-cueing problem, i.e. the detection-confirmation problem. In this setup, the goal is to maximize chances to detect...
New methods for team optimal decision, based on a stochastic agent coordination, are presented, implemented and tested. The methods are extensions of the cross-entropy algorithm (CE), initially dedicated to rare-event simulation. In particular, this paper investigates how the contributions of the agents could be involved in the simulation process. The approaches are tested on a SD-assignment problem,...
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...
This paper presents a methodology for the evaluation of a path planning algorithm based on a learning approach. Here this evaluation procedure is applied for the problem of optimizing the navigation of a mobile robot in a known environment. A metric map composed of landmarks representing natural elements is given to define the best trajectory which permits to guarantee a localization performance during...
This paper introduces a common method, based on the cross-entropy method, in order to solve a variety of search problems when search resources are scarce compared to the size of the space of search. In particular, we solve: detection and information search problems, a detection search game, and a two-targets detection search problem. Our approach is built of two steps: first, decompose a problem in...
This paper defines and implements a non-Bayesian fusion rule for combining densities of probabilities estimated by local (non-linear) filters for tracking a moving target by passive sensors. This rule is the restriction to a strict probabilistic paradigm of the recent and efficient proportional conflict redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing basic belief assignments...
This paper deals with the problem of optimizing the navigation of an intelligent mobile with respect to the maximization of the performance of the localization algorithm used during execution. It is assumed that a known map composed of features describing natural landmarks in the environment is given. The vehicle is also equipped with a range and bearing sensor to interact with its environment. The...
This paper addresses the problem of optimizing the navigation of an intelligent mobile in a real world environment, described by a map. The map is composed of features representing natural landmarks in the environment. The vehicle is equipped with a sensor which allows it to obtain range and bearing measurements from observed landmarks during the execution. These measurements are correlated with the...
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