| 引用本文: | 岳师光,查亚兵,尹全军,等.基于抽象隐马尔可夫模型的CGF路径规划识别.[J].国防科技大学学报,2014,36(1):148-153.[点击复制] |
| YUE Shiguang,ZHA Yabing,YIN Quanjun,et al.Path plan recognition by CGF based on abstract hidden Markov model[J].Journal of National University of Defense Technology,2014,36(1):148-153[点击复制] |
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| 基于抽象隐马尔可夫模型的CGF路径规划识别 |
| 岳师光, 查亚兵, 尹全军, 张琪 |
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(国防科技大学 信息系统与管理学院， 湖南 长沙 410073)
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| 摘要: |
| 路径规划识别是一种以位置信息为输入的在线识别。为了使CGF能在仿真中识别对手的路径和终点目标，在分析路径规划层次的基础上引入了抽象隐马尔可夫模型的识别框架。针对标准模型在对手更改终点目标和自上而下规划时无法识别的问题，提出了一种顶层策略可变的抽象隐马尔可夫模型。为模型的顶层策略增加初始分布和策略终止变量，更改了策略终止变量间的依赖关系，使下层策略能被强制终止。给出了改进后DBN结构，并通过推导条件概率更新和RB变量抽样流程实现了模型的近似推理。仿真实验表明，改进模型能准确识别给定环境下的各类典型航迹，不仅在终点目标不变时能较好地维持标准模型的识别准确率，在提供足够的观测数据后还能很好地解决变目标识别问题。 |
| 关键词: 计算机生成兵力 规划识别 路径规划 抽象隐马尔可夫模型 |
| DOI：10.11887/j.cn.201401026 |
| 投稿日期：2013-06-06 |
| 基金项目:国家自然科学基金资助项目（91024030） |
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| Path plan recognition by CGF based on abstract hidden Markov model |
| YUE Shiguang, ZHA Yabing, YIN Quanjun, ZHANG Qi |
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(College of Information System and Management, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Path plan recognition has been a kind of online recognition using positions as inputs. To allow CGF to recognize opponents’paths and destinations in simulation, a recognition framework of Abstract Hidden Markov Model is introduced following analyzing the hierarchy of path plan. Since it is difficult to recognize the path plans using standard model when destinations are changed and plans are executed from top to bottom, the Abstract Hidden Markov Model with Changeable Top-level Policy is proposed. The initial distribution and termination variables of top policy were given and the relations between policy termination variables were adjusted to allow the lower policy for a forced termination. The modified DBN structure was presented, and the approximate inference was realized by deducing processes of updating conditional probability and sampling RB variables as well. Simulation experiments show that different kinds of typical paths in specific environment can be recognized efficiently with this method. The modified model not only confirms good recognition accuracy compared with the standard model under the circumstance when destination is not changing, but also performs well in solving destination changing path plan recognition problems with sufficient observation data provided. |
| Keywords: computer generated forces plan recognition path plan abstract hidden markov model |
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