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Situation Assessment Approach Based on a Hierarchic Multi-timescale Bayesian Network

机译:基于分层多时间段贝叶斯网络的情况评估方法

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摘要

In this paper, situation assessment in the battle field is described by the modular Bayesian network, and a method is proposed for adaptive situation assessment using a hierarchic Bayesian Networks. For different levels, district network structures are adopted to infer situation and adaptively update parameters of network with different timescale. Specially, dynamic Bayesian networks are utilized in the lower level networks, taking full advantage of the direct measurement of sensors and improving the robustness of the assessment system. A simulation is provided to indicate how to structure the network model, infer situation and update parameters for hierarchic Bayesian networks. The simulation results illustrate the validity of the proposed method.
机译:在本文中,模块化贝叶斯网络描述了战斗领域的情况评估,并提出了一种使用层级贝叶斯网络进行自适应情况评估的方法。对于不同的级别,采用地区网络结构来推断出不同时间尺度的网络的情况和自适应更新网络参数。特别地,动态贝叶斯网络在较低级别的网络中使用,充分利用了传感器的直接测量和提高评估系统的鲁棒性。提供了一种模拟,以指示如何构建网络模型,推断情况和更新参数的分层贝叶斯网络。仿真结果说明了所提出的方法的有效性。

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