基于智能算法的目标威胁估计
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摘要
对空中来袭目标进行威胁估计是光电防御系统指挥决策活动中的重要环节，它是在目标识别的基础上，通过对目标的定性定量分析而进行的综合评价活动，为指挥员进行兵力部署和火力分配提供重要依据。现代防空作战环境要求目标威胁判断准确实时，若判断不准确，将导致目标分配失误，影响防空作战效能；判断不迅速，就会贻误战机。本文针对威胁估计的特性，采用现代智能算法，重点研究了光电对抗武器攻防中威胁估计技术及算法，本文的主要工作如下：
介绍了信息融合与威胁估计技术的基本理论和功能框架，对威胁估计技术的研究现状进行了归纳总结，阐述了威胁等级排序的内容和处理步骤，结合项目，通过分析光电防御系统作战过程，明确了系统决策的基本任务，并且由指挥控制系统功能，确定了辅助决策单元的内容。重点对威胁估计子模块的设计进行了研究，分析了威胁要素的提取方法，确定了威胁排序算法的工作流程及排序准则。
对经典智能算法，如微分进化算法(DE)、生物地理学优化算法(BBO)、粒子群算法(PSO)、布谷鸟搜索算法(CS)、蝙蝠算法(BA)和萤火虫算法(FA)，进行了深入的研究。在此基础上，结合其它智能优化技术，提出了DE/CS、HS/BA、MFA和BAM等新算法，实验结果表明，这些新算法明显提高了原算法的准确性。
针对神经网络在求解威胁估计时网络结构选择困难、过学习及泛化能力差的缺点，建立了Elman_AdaBoost强预测器目标威胁估计模型，并提出了基于该模型的算法。首先，根据AdaBoost算法和Elman神经网络的特点，提出了Elman_AdaBoost强预测器；然后，建立了一种新的Elman_AdaBoost强预测器目标威胁估计模型；最后，提出了基于Elman_AdaBoost强预测器目标威胁估计模型的算法。
针对萤火虫优化算法(GSO)和BP神经网络的特点，建立了一种基于GSO算法优化BP神经网络(GSOBP)的目标威胁估计模型，并提出了基于该模型的算法。该方法采用萤火虫算法优化BP神经网络的初始权值和阈值，优化后的BP神经网络能够更好地预测输出。结果表明，该方法的预测误差明显小于BP和PSO_SVM。
在对基本小波神经网络进行研究的基础上，采用改进小波神经网络-MWFWNN网络来解决威胁估计问题。提出了一种基于最小均方差选择小波基函数的方法，并构造了MWFWNN网络。建立了MWFWNN网络的目标威胁估计模型，提出了基于该模型的算法。首先建立小波基函数库，采用库中的小波基函数分别构造小波神经网络，根据相关公式更新小波基函数参数和网络权值。达到迭代次数后，采用训练集测试构造的小波神经网络，从中选择最优的小波基函数，构造MWFWNN网络。实验表明，该方法均方差为1.23×10-3，明显优于小波神经网络、BP网络及PSO_SVM。
Target threat assessment is a key issue in the collaborative multi-target attack. Itis a comprehensive assessment activity based on the qualitative and quantitativeanalyses of target, which can provide significant evidence for the commander tomake firepower allocation. In case of modern high-tech air defense combatenvironment, threat assessment of air target must be accurate and rapid. Aninaccurate judgment will lead to mistakes in decision-making and impact efficiencyof air defense operations. Adversely, prompt judgments affect the opportunity ofbattle. This dissertation provides an in-depth technical study of threat assessmenttechnology in the offensive and defensive of countermeasures of optoelectroniccountermeasure weapons using modern intelligent algorithms.
The main contributions of this dissertation are described as follows:
The basic theory of information fusion and threat assessment are introduced,and research and development status of threat assessment technology aresummarized. Workflow of threat assessment module and guidelines of threatsequencing are given according to application analyses of decision-making tasks andassistance decision-making in command and control system.
The in-depth research of classic intelligent algorithms, such as differentialevolution (DE), biogeography-based optimization (BBO), cuckoo search (CS),particle swarm optimization (PSO), bat algorithm (BA), and firefly algorithm (FA), is conducted. On this basis, several new hybrid algorithms, like DE/CS (differentialevolution/cuckoo search), HS/BA (harmony search/bat algorithm), MFA (modifiedfirefly algorithm), and BAM (bat algorithm with mutation), are initially proposed incombination with other intelligent optimization technology. The experimental resultson benchmarks show that these novel methods significantly improve theperformance of the original methods.
To improve the accuracy and usefulness of the target threat assessment in the aircombat, a target threat assessment model and algorithm based on Elman_AdaBooststrong predictor is originally proposed. Firstly, the Elman_AdaBoost strongpredictor is introduced; secondly, a target threat assessment model based onElman_AdaBoost strong predictor is established; at last, an algorithm is described.The experimental results show that the prediction error for Elman_AdaBoost strongpredictor algorithm is notably lower than the weak predictor.
Moreover, to further enhance the accuracy and feasibility of the target threatassessment in the aerial combat, another target threat assessment model andalgorithm based on back-propagation (BP) neural network optimized by glowwormswarm optimization (GSOBP) algorithm is initially proposed. In GSOBP, GSO isused to simultaneously optimize the initial weights and thresholds of BP neuralnetwork. Target threat database is adopted to study the target threat predictionperformance of GSOBP, and the proposed method is also compared with BP andPSO_SVM. The experiment indicates that GSOBP has higher target threat predictionaccuracy than the normal BP and PSO_SVM.
A variant of wavelet neural networks (WNN)-MWFWNN network is firstlyproposed to solve threat assessment more accurately, efficiently and effectively inthe aerial combat. How to select the appropriate wavelet function is adifficult-to-solve problem when constructing wavelet neural network. Thisdissertation proposes a wavelet mother function selection algorithm in terms ofminimum mean squared error, and then constructs MWFWNN network using theabove algorithm. Firstly, a wavelet function library is established; secondly, wavelet neural network is constructed with each wavelet mother function in the library, andupdate wavelet function parameters and the network weights according to therelevant modifying formula. The constructed wavelet neural network is trained withtraining set, and then optimal wavelet function with minimum mean squared error ischosen to build MWFWNN network. Experimental results show that the meansquared error is1.23×10-3, which is better than WNN, BP and PSO_SVM.
介绍了信息融合与威胁估计技术的基本理论和功能框架，对威胁估计技术的研究现状进行了归纳总结，阐述了威胁等级排序的内容和处理步骤，结合项目，通过分析光电防御系统作战过程，明确了系统决策的基本任务，并且由指挥控制系统功能，确定了辅助决策单元的内容。重点对威胁估计子模块的设计进行了研究，分析了威胁要素的提取方法，确定了威胁排序算法的工作流程及排序准则。
对经典智能算法，如微分进化算法(DE)、生物地理学优化算法(BBO)、粒子群算法(PSO)、布谷鸟搜索算法(CS)、蝙蝠算法(BA)和萤火虫算法(FA)，进行了深入的研究。在此基础上，结合其它智能优化技术，提出了DE/CS、HS/BA、MFA和BAM等新算法，实验结果表明，这些新算法明显提高了原算法的准确性。
针对神经网络在求解威胁估计时网络结构选择困难、过学习及泛化能力差的缺点，建立了Elman_AdaBoost强预测器目标威胁估计模型，并提出了基于该模型的算法。首先，根据AdaBoost算法和Elman神经网络的特点，提出了Elman_AdaBoost强预测器；然后，建立了一种新的Elman_AdaBoost强预测器目标威胁估计模型；最后，提出了基于Elman_AdaBoost强预测器目标威胁估计模型的算法。
针对萤火虫优化算法(GSO)和BP神经网络的特点，建立了一种基于GSO算法优化BP神经网络(GSOBP)的目标威胁估计模型，并提出了基于该模型的算法。该方法采用萤火虫算法优化BP神经网络的初始权值和阈值，优化后的BP神经网络能够更好地预测输出。结果表明，该方法的预测误差明显小于BP和PSO_SVM。
在对基本小波神经网络进行研究的基础上，采用改进小波神经网络-MWFWNN网络来解决威胁估计问题。提出了一种基于最小均方差选择小波基函数的方法，并构造了MWFWNN网络。建立了MWFWNN网络的目标威胁估计模型，提出了基于该模型的算法。首先建立小波基函数库，采用库中的小波基函数分别构造小波神经网络，根据相关公式更新小波基函数参数和网络权值。达到迭代次数后，采用训练集测试构造的小波神经网络，从中选择最优的小波基函数，构造MWFWNN网络。实验表明，该方法均方差为1.23×10-3，明显优于小波神经网络、BP网络及PSO_SVM。
Target threat assessment is a key issue in the collaborative multi-target attack. Itis a comprehensive assessment activity based on the qualitative and quantitativeanalyses of target, which can provide significant evidence for the commander tomake firepower allocation. In case of modern high-tech air defense combatenvironment, threat assessment of air target must be accurate and rapid. Aninaccurate judgment will lead to mistakes in decision-making and impact efficiencyof air defense operations. Adversely, prompt judgments affect the opportunity ofbattle. This dissertation provides an in-depth technical study of threat assessmenttechnology in the offensive and defensive of countermeasures of optoelectroniccountermeasure weapons using modern intelligent algorithms.
The main contributions of this dissertation are described as follows:
The basic theory of information fusion and threat assessment are introduced,and research and development status of threat assessment technology aresummarized. Workflow of threat assessment module and guidelines of threatsequencing are given according to application analyses of decision-making tasks andassistance decision-making in command and control system.
The in-depth research of classic intelligent algorithms, such as differentialevolution (DE), biogeography-based optimization (BBO), cuckoo search (CS),particle swarm optimization (PSO), bat algorithm (BA), and firefly algorithm (FA), is conducted. On this basis, several new hybrid algorithms, like DE/CS (differentialevolution/cuckoo search), HS/BA (harmony search/bat algorithm), MFA (modifiedfirefly algorithm), and BAM (bat algorithm with mutation), are initially proposed incombination with other intelligent optimization technology. The experimental resultson benchmarks show that these novel methods significantly improve theperformance of the original methods.
To improve the accuracy and usefulness of the target threat assessment in the aircombat, a target threat assessment model and algorithm based on Elman_AdaBooststrong predictor is originally proposed. Firstly, the Elman_AdaBoost strongpredictor is introduced; secondly, a target threat assessment model based onElman_AdaBoost strong predictor is established; at last, an algorithm is described.The experimental results show that the prediction error for Elman_AdaBoost strongpredictor algorithm is notably lower than the weak predictor.
Moreover, to further enhance the accuracy and feasibility of the target threatassessment in the aerial combat, another target threat assessment model andalgorithm based on back-propagation (BP) neural network optimized by glowwormswarm optimization (GSOBP) algorithm is initially proposed. In GSOBP, GSO isused to simultaneously optimize the initial weights and thresholds of BP neuralnetwork. Target threat database is adopted to study the target threat predictionperformance of GSOBP, and the proposed method is also compared with BP andPSO_SVM. The experiment indicates that GSOBP has higher target threat predictionaccuracy than the normal BP and PSO_SVM.
A variant of wavelet neural networks (WNN)-MWFWNN network is firstlyproposed to solve threat assessment more accurately, efficiently and effectively inthe aerial combat. How to select the appropriate wavelet function is adifficult-to-solve problem when constructing wavelet neural network. Thisdissertation proposes a wavelet mother function selection algorithm in terms ofminimum mean squared error, and then constructs MWFWNN network using theabove algorithm. Firstly, a wavelet function library is established; secondly, wavelet neural network is constructed with each wavelet mother function in the library, andupdate wavelet function parameters and the network weights according to therelevant modifying formula. The constructed wavelet neural network is trained withtraining set, and then optimal wavelet function with minimum mean squared error ischosen to build MWFWNN network. Experimental results show that the meansquared error is1.23×10-3, which is better than WNN, BP and PSO_SVM.
引文
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