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摘要:
战术活动识别是战场态势感知的重要研究内容。为提高战术活动识别的准确性与实时性，提出了一种基于上下文独立动态贝叶斯网络（CIDBN）的战术活动识别模型及在线精确推理。通过对战术活动机制的分析，采用动态贝叶斯网络（DBN）理论，建立了一个初始战术活动识别模型。该模型引入了威胁指数节点来影响战术活动的终止与选择，并采用模糊隶属度函数对连续变量进行离散化处理。依据上下文独立关系对该模型进行简化，获得了一个基于CIDBN的战术活动识别模型。将接口算法扩展于该模型上，提出了在线精确推理算法。仿真结果表明，所提出的战术活动识别方法，具有识别精度高、较低不确定性和实时性高的优点。
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关键词:
- 动态贝叶斯网络(DBN) /
- 接口算法 /
- 上下文独立 /
- 威胁指数 /
- 精确推理
Abstract:Tactical activity recognition is an important research content of battlefield situational awareness. In order to improve the accuracy and real-time of tactical activity recognition, a tactical activity recognition model and online accurate reasoning based on Context-Independent Dynamic Bayesian Network (CIDBN) are put forward. Based on the analysis of tactical activity mechanism and Dynamic Bayesian Network (DBN) theory, an initial tactical activity recognition model is established. In this model, threat index nodes are introduced to influence the termination and selection of activities, and the fuzzy membership function is used to discretize the continuous variables. The model is simplified based on the relationship of context independence, and the new tactical activity recognition model based on CIDBN is obtained. The interface algorithm is extended to the model and an online accurate reasoning algorithm is proposed. The simulation results show that the proposed tactical activity recognition method has the advantages of high recognition accuracy, low uncertainty and high real-time performance.
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图 5 基于上下文独立的活动变量分解
Figure 5. Activity variable decomposition based on context independence
图 9 超视距空战想定示意图
Figure 9. Schematic diagram of beyond visual range of air combat scenarios
图 11 滤波轨迹与各个运动状态估计
Figure 11. Filtering trajectory and various motion state estimation
表 1 战术活动识别的结果
Table 1. Results of tactical activity recognition
测试 HMM CRF CIDBN Acc U Acc U Acc U T-1 0.842 5 1.642 0 0.913 8 1.185 0 0.944 9 1.020 6 T-2 0.865 7 1.969 6 0.921 4 1.410 3 0.952 2 1.012 3 
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表 2 平均单步运行所需时间
Table 2. Average time of one-step running
算法 平均的单步推理时间/s 团结树算法(DBN) 0.007t+0.01 边界算法(DBN) 0.092 9 向前算法(DBN) 0.207 0 接口算法(DBN) 0.065 3 接口算法(CIDBN) 0.025 3
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