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Multi-Level Information Fusion Approach with Dynamic Bayesian Networks for an Active Perception of the environment

机译：动态贝叶斯网络的多层信息融合方法，用于主动感知环境

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Most Situation Awareness applications in Information Fusion try to evaluate a dynamic environment by a passive approach combining heterogeneous information. However in a crisis situation, the decision has to be made efficiently as the world quickly evolves. A consequence is the difficulty to get the information in a fast and efficient way with an acceptable confidence. Another problem is the processing of a significant amount of heterogeneous information in near real time. To address this issue, we propose a multi-level Information Fusion framework based on Dynamic Bayesian Networks (DBN) with an active perception approach. The contribution of this model primarily lies in its capability of handling both Hard & Soft sensors and resulting information. And secondly in the identification of the most valuable DBN variables which maximize the information gains in the next step. These valuable variables allow to infer states on a sub-DBN to reduce the complexity of calculation. In this top-down approach, we seek which variables can provide the most valuable information to select automatically the right sensors and choose correct actions to optimally observe these variables. We finally propose an illustration with a basic maritime scenario.
机译：信息融合中的大多数情况感知应用程序都试图通过结合异构信息的被动方法来评估动态环境。但是，在危机情况下，必须随着世界的快速发展有效地做出决定。结果是难以以可接受的置信度快速有效地获取信息。另一个问题是近实时地处理大量异构信息。为了解决这个问题，我们提出了一种基于动态贝叶斯网络（DBN）的多层信息融合框架，并采用了一种主动感知方法。该模型的贡献主要在于它既可以处理硬传感器也可以处理软传感器，并且可以处理所得到的信息。其次，确定最有价值的DBN变量，这些变量将使下一步的信息获取最大化。这些有价值的变量允许推断子DBN上的状态，以降低计算的复杂性。在这种自上而下的方法中，我们寻求哪些变量可以提供最有价值的信息，以自动选择正确的传感器，并选择正确的操作以最佳地观察这些变量。我们最后提出一个带有基本海上情景的插图。

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