变结构DDBNs的推理算法与多目标识别

 目前变结构离散动态贝叶斯网络（DDBNs）的推理算法存在的缺陷是计算量随时间片数的增加呈指数增长。为了解决这类网络的推理问题,引入前向后向算法的基本思想,提出一种新的变结构DDBNs的推理算法。在分析变结构DDBNs数据结构的基础上,定义变结构DDBNs的前向、后向算子,从理论上对算法进行了推导,它的计算量仅与时间片数成线性关系。并且把该算法应用于识别空中多目标的变结构DDBNs,通过有效融合“交战行为"节点信息,使识别系统的鲁棒性显著增强。仿真结果验证了推理算法的有效性。

Inference Algorithm of Variable Structure DDBNs and Multi-target Recognition

The current inference algorithm on variable structure discrete dynamic Bayesian networks (DDBNs) suffers from the drawback of exponential growth in complexity as time slices increase. To solve this problem, this article introduces the basic idea of a forward-backward algorithm, and proposes a new inference algorithm of variable structure DDBNs. On the basis of an analysis of the data structure of the variable structure networks, the forward operator and backward operator of variable structure DDBNs are defined and the algorithm deduced in theory, whose amount of calculation has a linear relationship with the number of the time slices. In addition, the algorithm is applied to variable structure DDBNs to identify air multi-targets. By fusing the information of the “engagement behavior” node efficiently, the robustness of the identifying system is strengthened significantly. The validity of this algorithm is proved by the simulation results.