The hierarchical SVDDBNS based on modularization concept for air target recognition

Hao Fan, Xiaguang Gao, Haiyang Chen
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引用次数: 0

Abstract

The process of Air target identification is hierarchical, and is also a process of data fusion of diversified information obtained in the unstable time domain. In this paper, the process of air target recognition is regarded as a process of a qualitative inference. According to the features that the hierarchy of air target identification process and the input parameters obtained in the unstable time domain, we constructed the air target recognition model on hierarchical structure-varied discrete dynamic bayesian networks (hierarchical SVDDBNs) by modularization concept. The air target identification model has such features, that is , the constructed model can real-time reconstructed the networks and finish the tasks flexibly by the features of the input data. In constructing bayesian networks model, the changes of structure is regular, in addition, the number of network nodes don't influence the decouple of the state of network nodes each other. Such can avoid structure learning and parameters learning. In the paper, the inference algorithm is presented, and simulation results show the feasibility of this approach.
基于模块化概念的分层SVDDBNS空中目标识别
空中目标识别的过程是层次化的,也是在不稳定时域中获取的多种信息进行数据融合的过程。本文将空中目标识别过程视为一个定性推理的过程。针对空中目标识别过程的层层化特点和输入参数在不稳定时域内获取的特点,采用模块化思想构建了基于分层变结构离散动态贝叶斯网络(hierarchical svddbn)的空中目标识别模型。空中目标识别模型具有这样的特点,即所构建的模型可以利用输入数据的特征实时重构网络,灵活地完成任务。在构建贝叶斯网络模型时,网络结构的变化是有规律的,而且网络节点的数量不影响网络节点之间状态的解耦。这样可以避免结构学习和参数学习。文中给出了推理算法,仿真结果表明了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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