基于贝叶斯网络的UCAV编队对地攻击智能决策研究
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摘要
UCAV编队对地攻击战术决策是一个涉及多学科相互交叉、融合的研究方向，目前国内还没有进行系统的研究，国外的研究也在试验验证阶段。UCAV编队对地攻击决策系统作为UCAV编队作战的核心系统，如何在瞬息万变的战场环境中，根据收集到的不确定性信息进行及时有效地推理是一个亟待解决的问题。论文以“十五”国防预研项目为支撑，在陕西省自然科学基金“基于贝叶斯网络的复杂系统智能决策理论与方法研究”的资助下，以实现快速、正确的UCAV编队对地攻击智能决策为目标，基于贝叶斯网络强大的不确定性推理能力和图形表示的优越性，针对UCAV编队对地攻击智能决策系统的核心问题进行研究。研究工作主要包括如下几个方面：
1) UCAV编队对地攻击智能决策系统总体研究
在分析UCAV编队对地攻防对抗系统组成的基础上，提出了UCAV编队对地攻击的系统结构和功能划分，给出了UCAV编队对地攻击的作战流程与攻击方式；针对UCAV编队对地攻击的复杂性、层次性和知识的分布性，提出了UCAV编队对地攻击决策的信息交互策略、所需的知识类型、知识获取模型及其知识表示方法；根据UCAV编队对地作战的决策类型和决策方式，构造了UCAV编队对地攻击智能决策系统并且提出了决策系统应该解决的关键技术。
2)基于决策图与贝叶斯网络的概率分布估计算法的性能研究
决策图贝叶斯优化算法是基于决策图与贝叶斯网络的一种概率分布估计算法。论文不但对算法的流程及其主要的操作进行了详细分析，而且对关键参数对算法性能的影响进行了定量研究。论文还通过将该算法与基本遗传算法、粒子群算法和贝叶斯优化算法的比较分析验证了该算法在解决层次可分优化问题上的优越性。
3)基于贝叶斯网络的UCAV编队对地态势估计研究
对UCAV编队对地态势估计的空间表示和数学模型进行了分析研究，利用贝叶斯网络适于多传感器数据融合的特征，提出采用贝叶斯网络来进行UCAV编队对地分布式的态势估计研究。建立了基于贝叶斯网络的UCAV对地战术态势估计模型，提出了应用贝叶斯推理算法进行多源信息融合的推理方法和贝叶斯网络的灵敏度分析方法，并且进行了仿真分析。
4)基于决策图贝叶斯优化算法的UCAV编队对地协同任务分配研究
提出了基于决策图贝叶斯优化算法的UCAV编队对地协同任务分配算法，建立了UCAV编队对地攻击的协同任务分配模型，该模型不但考虑了电子对抗对编队任务分配的影响，并且引入距离折扣因子来处理目标距离较近而价值相对较小的目标比目标距离较远而价值较大的目标更加重要的问题。仿真实例表明贝叶斯优化算法收敛速度快，能够收敛到全局最优解，并且对是否考虑电子干扰以及是否考虑距离折扣因子的仿真结果的分析比较也验证了电子干扰和距离折扣因子引入的正确性和实用性。
5)基于影响图的UCAV编队对地战术任务决策研究
提出了基于影响图的UCAV编队对地攻击任务决策算法。影响图是对动态贝叶斯网络的扩展，它在贝叶斯网络的基础上增加了决策节点和效用节点。由于UCAV编队对地攻击战术任务决策是充满不确定性的复杂决策问题，因此提出基于影响图模型并结合效用理论来解决编队对地战术任务决策问题，建立了UCAV编队对地攻击战术任务决策的系统结构和数学模型以及对应的影响图模型，并且进行了仿真分析。
6)基于模糊贝叶斯网络的UCAV编队对地攻击战斗损伤评估研究
提出了基于模糊贝叶斯网络的UCAV编队对地攻击损伤评估算法。模糊贝叶斯网络综合了模糊逻辑与贝叶斯网络的优点，将贝叶斯网络的清晰节点变量推广到了模糊节点变量。论文提出了解决损伤评估的模糊贝叶斯网络方法，建立了损伤评估的模糊贝叶斯网络模型并且进行了仿真分析。
论文通过对UCAV编队对地攻击智能决策系统的分析，描述了未来信息化战场中UCAV编队对地攻击的决策模式，通过贝叶斯网络对各关键技术进行了仿真建模分析，根据算例仿真证明了各模型和算法的有效性，为提高UCAV编队对地决策系统过程的自动化和智能化程度打下了坚实的基础。
The research of decision-making of the UCAV teams air-to-ground attack(UCAVTAGA) covers and integrates many scientific fields. Therefore, no systemicresearch exists in our country at present and such research in other countries is also inthe experimental validation phase. The decision-making system of the UCAVTAGA isthe central system of the UCAV teams. How to make an effective inference according tothe uncertain evidence acquired is a problem needed an urgent solvent. This paper issupported by the fund of Shanxi Natural Science. Aiming at realizing a quick andcorrect intelligent decision-making of the UCAVTAGA, this paper studied the mainproblems of UCAVTAGA based on the powerful inference capability of the Bayesiannetwork (BNs) and the advantage of the graph in expression. The research is dividedinto the following 7 parts:
1) Study on the Intelligent Decision-making System of UCAVTAGA
The system's structure and functional classification of UCAVTAGA have beenfirstly presented based on the consistence of the attack-defense confront system. Thecombat flow and attack mode of UCAVTAGA have been put forward. Interactivestrategy of information, knowledge type required, knowledge achieve mode andknowledge expression of the UCAVTAGA has been put forward toward the complexityand distribution of knowledge. The intellectual decision-making system of theUCAVTAGA and the key technology required in the decision-making system has beenalso proposed.
2) Study on the Performance of Probability Distribution Estimation Based onDecision Graph and Bayesian Networks
Decision-Graph Bayesian Optimization Algorithm (DBOA) is a probabilitydistribution estimation algorithm. The flow and primary operation of the algorithm havebeen analyses in detail. Moreover, the effect on the performance of the algorithm causedby the key parameters has been conducted a quantitive research. The superiority insolving the decomposable problems has been validated with comparing with the geneticalgorithm, PSO and Bayesian optimization algorithm.
3) Study on Distributed Situation Assessment of UCAVTAGA Based on BN
The space expression and mathematical mode of the situation assessment of theUCAVTAGA have been researched. Due to the BN is adapted to the multi-sensor fusion,the distribution situation assessment of the UCAVTAGA using Bayesian network hasbeen brought forward. Tactical situation assessment mode of UCAVTAGA based on BNhas been built. Multi-source information fusion inference algorithm and sensitiveanalysis method using Bayesian network have been conducted and simulated.
4) Study on the Cooperate Task Assignment of UCAVTAGA Based on DBOA
Task assignment algorithm of UCAVTAGA based on DBOA is presented. Theeffect caused by electronic confront has been taken into account. The concept ofDistance Discount Factor (DDF) has been introduced to address the fact that targetingclose but less significant units could be more rewarding than targeting far but moresignificant units. Simulation results have verified that the method could be used to solvethe complex question, the operation was quickly and the solution was best and the DDFis validity with comparing the simulation results with and without DDF.
5) Study on the Tactical Task Decision-making of UCAVTAGA Based onInfluence Graph (IG)
Tactical task decision-making method of the UCAVTAGA based on IG has beenbrought out. IG expands the dynamic Bayesian network with adding the decision nodeand value node to the Bayesian network. Because the decision-making of theUCAVTAGA is full of uncertainty, assistant decision-making in order to solve theproblem of dynamic tactical task decision-making is put forward based on IG mode andcombined with the value theory. The system structure, mathematical mode and relevantIG mode of the tactical task decision-making of UCAVTAGA have been built and thesimulation analysis has been conducted.
6) Study on the Battle Damage Assessment of UCAVTAGA Based Fuzzy BN
Battle damage assessment (BDA) algorithm based on the fuzzy Bayesian networkis presented for the first time. Fuzzy Bayesian network integrates the advantages of thefuzzy logic and BN, converting the clear node variable to fuzzy node variable. Thispaper also presents a method to solve the BDA based on BN. BN mode of BDA hasbeen built and simulated.
This paper depicts the decision-making mode of the UCAVTAGA in the futurebattlefield through the analysis of intellectual decision-making system. Moreover, thekey technology is simulated and modeled based on BN and the effectiveness of themode and algorithm is proved according to the simulation, which providing a solidfoundation for the analysis and improvement of the automation and intelligence of thedecision-making process of UCAVTAGA.
1) UCAV编队对地攻击智能决策系统总体研究
在分析UCAV编队对地攻防对抗系统组成的基础上，提出了UCAV编队对地攻击的系统结构和功能划分，给出了UCAV编队对地攻击的作战流程与攻击方式；针对UCAV编队对地攻击的复杂性、层次性和知识的分布性，提出了UCAV编队对地攻击决策的信息交互策略、所需的知识类型、知识获取模型及其知识表示方法；根据UCAV编队对地作战的决策类型和决策方式，构造了UCAV编队对地攻击智能决策系统并且提出了决策系统应该解决的关键技术。
2)基于决策图与贝叶斯网络的概率分布估计算法的性能研究
决策图贝叶斯优化算法是基于决策图与贝叶斯网络的一种概率分布估计算法。论文不但对算法的流程及其主要的操作进行了详细分析，而且对关键参数对算法性能的影响进行了定量研究。论文还通过将该算法与基本遗传算法、粒子群算法和贝叶斯优化算法的比较分析验证了该算法在解决层次可分优化问题上的优越性。
3)基于贝叶斯网络的UCAV编队对地态势估计研究
对UCAV编队对地态势估计的空间表示和数学模型进行了分析研究，利用贝叶斯网络适于多传感器数据融合的特征，提出采用贝叶斯网络来进行UCAV编队对地分布式的态势估计研究。建立了基于贝叶斯网络的UCAV对地战术态势估计模型，提出了应用贝叶斯推理算法进行多源信息融合的推理方法和贝叶斯网络的灵敏度分析方法，并且进行了仿真分析。
4)基于决策图贝叶斯优化算法的UCAV编队对地协同任务分配研究
提出了基于决策图贝叶斯优化算法的UCAV编队对地协同任务分配算法，建立了UCAV编队对地攻击的协同任务分配模型，该模型不但考虑了电子对抗对编队任务分配的影响，并且引入距离折扣因子来处理目标距离较近而价值相对较小的目标比目标距离较远而价值较大的目标更加重要的问题。仿真实例表明贝叶斯优化算法收敛速度快，能够收敛到全局最优解，并且对是否考虑电子干扰以及是否考虑距离折扣因子的仿真结果的分析比较也验证了电子干扰和距离折扣因子引入的正确性和实用性。
5)基于影响图的UCAV编队对地战术任务决策研究
提出了基于影响图的UCAV编队对地攻击任务决策算法。影响图是对动态贝叶斯网络的扩展，它在贝叶斯网络的基础上增加了决策节点和效用节点。由于UCAV编队对地攻击战术任务决策是充满不确定性的复杂决策问题，因此提出基于影响图模型并结合效用理论来解决编队对地战术任务决策问题，建立了UCAV编队对地攻击战术任务决策的系统结构和数学模型以及对应的影响图模型，并且进行了仿真分析。
6)基于模糊贝叶斯网络的UCAV编队对地攻击战斗损伤评估研究
提出了基于模糊贝叶斯网络的UCAV编队对地攻击损伤评估算法。模糊贝叶斯网络综合了模糊逻辑与贝叶斯网络的优点，将贝叶斯网络的清晰节点变量推广到了模糊节点变量。论文提出了解决损伤评估的模糊贝叶斯网络方法，建立了损伤评估的模糊贝叶斯网络模型并且进行了仿真分析。
论文通过对UCAV编队对地攻击智能决策系统的分析，描述了未来信息化战场中UCAV编队对地攻击的决策模式，通过贝叶斯网络对各关键技术进行了仿真建模分析，根据算例仿真证明了各模型和算法的有效性，为提高UCAV编队对地决策系统过程的自动化和智能化程度打下了坚实的基础。
The research of decision-making of the UCAV teams air-to-ground attack(UCAVTAGA) covers and integrates many scientific fields. Therefore, no systemicresearch exists in our country at present and such research in other countries is also inthe experimental validation phase. The decision-making system of the UCAVTAGA isthe central system of the UCAV teams. How to make an effective inference according tothe uncertain evidence acquired is a problem needed an urgent solvent. This paper issupported by the fund of Shanxi Natural Science. Aiming at realizing a quick andcorrect intelligent decision-making of the UCAVTAGA, this paper studied the mainproblems of UCAVTAGA based on the powerful inference capability of the Bayesiannetwork (BNs) and the advantage of the graph in expression. The research is dividedinto the following 7 parts:
1) Study on the Intelligent Decision-making System of UCAVTAGA
The system's structure and functional classification of UCAVTAGA have beenfirstly presented based on the consistence of the attack-defense confront system. Thecombat flow and attack mode of UCAVTAGA have been put forward. Interactivestrategy of information, knowledge type required, knowledge achieve mode andknowledge expression of the UCAVTAGA has been put forward toward the complexityand distribution of knowledge. The intellectual decision-making system of theUCAVTAGA and the key technology required in the decision-making system has beenalso proposed.
2) Study on the Performance of Probability Distribution Estimation Based onDecision Graph and Bayesian Networks
Decision-Graph Bayesian Optimization Algorithm (DBOA) is a probabilitydistribution estimation algorithm. The flow and primary operation of the algorithm havebeen analyses in detail. Moreover, the effect on the performance of the algorithm causedby the key parameters has been conducted a quantitive research. The superiority insolving the decomposable problems has been validated with comparing with the geneticalgorithm, PSO and Bayesian optimization algorithm.
3) Study on Distributed Situation Assessment of UCAVTAGA Based on BN
The space expression and mathematical mode of the situation assessment of theUCAVTAGA have been researched. Due to the BN is adapted to the multi-sensor fusion,the distribution situation assessment of the UCAVTAGA using Bayesian network hasbeen brought forward. Tactical situation assessment mode of UCAVTAGA based on BNhas been built. Multi-source information fusion inference algorithm and sensitiveanalysis method using Bayesian network have been conducted and simulated.
4) Study on the Cooperate Task Assignment of UCAVTAGA Based on DBOA
Task assignment algorithm of UCAVTAGA based on DBOA is presented. Theeffect caused by electronic confront has been taken into account. The concept ofDistance Discount Factor (DDF) has been introduced to address the fact that targetingclose but less significant units could be more rewarding than targeting far but moresignificant units. Simulation results have verified that the method could be used to solvethe complex question, the operation was quickly and the solution was best and the DDFis validity with comparing the simulation results with and without DDF.
5) Study on the Tactical Task Decision-making of UCAVTAGA Based onInfluence Graph (IG)
Tactical task decision-making method of the UCAVTAGA based on IG has beenbrought out. IG expands the dynamic Bayesian network with adding the decision nodeand value node to the Bayesian network. Because the decision-making of theUCAVTAGA is full of uncertainty, assistant decision-making in order to solve theproblem of dynamic tactical task decision-making is put forward based on IG mode andcombined with the value theory. The system structure, mathematical mode and relevantIG mode of the tactical task decision-making of UCAVTAGA have been built and thesimulation analysis has been conducted.
6) Study on the Battle Damage Assessment of UCAVTAGA Based Fuzzy BN
Battle damage assessment (BDA) algorithm based on the fuzzy Bayesian networkis presented for the first time. Fuzzy Bayesian network integrates the advantages of thefuzzy logic and BN, converting the clear node variable to fuzzy node variable. Thispaper also presents a method to solve the BDA based on BN. BN mode of BDA hasbeen built and simulated.
This paper depicts the decision-making mode of the UCAVTAGA in the futurebattlefield through the analysis of intellectual decision-making system. Moreover, thekey technology is simulated and modeled based on BN and the effectiveness of themode and algorithm is proved according to the simulation, which providing a solidfoundation for the analysis and improvement of the automation and intelligence of thedecision-making process of UCAVTAGA.
引文
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