假目标配置数量模型及其效费比分析

在现代战争中,示假伪装的地位越来越重要。引入了真假目标的发现识别概率的指数分布模型,综合考虑作战时间、真假目标价格比等因素,确定战场生存能力和配置假目标成本两个目标函数,并以最小加权偏差法确定两者权重,建立基于多目标决策的假目标配置数量模型。最后结合实际问题,将各参数条件带入模型计算出假目标的最佳配置数量,同时利用假目标的效费比模型进行检验分析,从而为战时假目标配置数量的确定提供参考依据。     

多目标多火力通道云调度策略研究

在水面舰艇面对多目标饱和攻击时,对多火力通道的最佳调度是水面舰艇自防御武器系统火力分配的关键问题。本文采用云模型方法将多目标威胁判断与反导武器系统多火力通道状态相匹配,通过实时评估目标威胁等级、武器系统抗击时间和最佳抗击距离等因素,提出一种兼顾目标和火力通道实时状态的新调度策略——云调度策略,有效降低舰艇损伤程度,提高反导武器系统抗击效率和抗击质量。仿真证明,此策略可提高整体反导防御效能。     

Interval uncertain optimization for damping fluctuation of asegmented electromagnetic buffer under intensive impact load

Aiming at the problems of demagnetization effect of electromagnetic buffer (EMB) caused by high ve-locity under intensive impact load and the difficulty and error of machining composite thin-walled long tube, a segmented EMB is proposed. The inner tube and air-gap are divided into initial segments and the traversing segments. Through theoretical analysis, impact test and simulation, it can be found that the RRF curve has two peaks. Firstly, in order to reduce the resultant resistance force (RRF) peaks, the sensitivity analysis based on optimal Latin hypercube design (OLHD) and polynomial regression was performed. The results show that the smallest contribution ratio to the dynamic response is the seventh and ninth segments of the inner tube, which are less than 1％. Then, fully considering the uncertain factors, important parameters are selected for uncertain optimization after sensitivity analysis. The in-terval order and interval probability degree methods are used to establish interval uncertain optimiza-tion model of the RRF considering robustness. The model was solved using an interval nested optimization method based on radial basis function (RBF) neural network. Finally, the Pareto front is obtained and numerical simulation is performed to verify the optimal value. It indicates that the two kinds of RRF peak is obviously reduced, and the optimization object and strategy are effective.     

A cooperative interference resource allocation method based on improved firefly algorithm

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more sci-entific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).