A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into sever...     

Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Trajectory data mining is widely used in military and civil applications, such as early warning and surveillance system, intelligent traffic system and so on. Through trajectory similarity measurement and clustering, target behavior patterns can be found from massive spatiotemporal trajectory data. In order to mine frequent behaviors of targets from complex historical trajectory data, a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion is proposed in this paper. Firstly, spatial–temporal Hausdorff distance is proposed to measure multidimensional information differences of spatiotemporal trajectories, which can distinguish the behaviors with similar location but different course and velocity. On this basis, by combining the idea of k-nearest neighbor and density peak clustering, a new trajectory clustering algorithm is proposed to mine behavior patterns from trajectory data with uneven density distribution. Finally, we implement the proposed algorithm in simulated and radar measured trajectory data respectively. The experimental results show that the proposed algorithm can mine target behavior patterns from different complex application scenarios more quickly and accurately compared to the existing methods, which has a good application prospect in intelligent monitoring tasks.     

基于FAHP与灰色聚类的 航空地面电源车功能损伤状态评估 aaa

针对战损情况下航空地面电源车功能损伤状态评估的问题,以电源车的功能结构层次为基础建立了指标体系,应用改进的模糊层次分析法(Fuzzy Analytical Hierarchy Process,FAHP)法确定了指标权重。建立了灰色聚类评估模型,并提出了指标灵敏度分析方法。针对某型战损电源车的功能损伤状态进行了评估,得出了功能损伤状态聚类评估结果,分析了指标权重变化对评估结果的影响,验证了方法的可行性和有效性。     

适用于长时间烧蚀防热的蜂窝增强低密度材料

针对长时间烧蚀防热对防热材料的需求,研制了两种蜂窝增强低密度烧蚀防热材料,采用电弧风
洞加热设备对其防热性能进行了考核,并对其力学及热物理性能进行了研究。结果表明,在两种长时间(1 000
s 左右)热环境的考核下,蜂窝增强低密度材料烧蚀后背面温度均低于200℃,表面碳层均匀、完整,无明显烧蚀
量,显示了优异的隔热性能和良好的抗气流冲刷能力。     

Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.     

基于DOA聚类算法的ARM抗有源诱偏技术研究

有源诱偏技术是雷达对抗反辐射导弹的一种重要手段,可以大大降低反辐射导弹的作战效能.为提高对敌目标的精确打击能力,在分析闪烁诱饵诱偏原理的基础上,针对有源诱偏干扰下被动雷达测角精度与稳定度不高的问题,通过对有源诱偏信号的时域特征进行分析,提出一种基于脉冲前沿检测的 DOA 聚类分选算法,找出前沿超前的辐射源信号,实现了高...     

Ultrasonic constitutive model and its application in ultrasonic vibration-assisted milling Ti3Al intermetallics

Ultrasonic vibration-assisted technology is widely utilized in the performance research and manufacturing process of metallic materials owing to its advantages of introducing highfrequency acoustic systems. However, the acoustic plasticity constitutive model and potential mechanism, involving Ti₃Al intermetallic compounds, have not yet been clarified. Therefore, the Ultrasonic-K-M hybrid acoustic constitutive model of Ti₃Al was established by considering the stress superpos...     

A clustering based method to complete frame of discernment

When the existing information does not contain all categories, the Generalized Evidence Theory (GET) can deal with information fusion. However, the question of how to determine the number of categories through GET is still intriguing. To address this question, a modified k-means clustering, named centers initialized clustering is proposed, filling the gap of identification and complement of the frame of discernment. Based on this clustering method, the number of categories is determined. The initialized centers selected by center density keep the cluster results constant, enhancing the stability of clustering results. Besides, constructing Generalized basic Probability Assignment (GBPA) modules in a conservative way improves the reliability of the results. The mass of empty set in combined GBPAs is the indicator of the number of categories. Experiments on real and artificial data sets are conducted to show the effectiveness.     

数据驱动的运载火箭氧涡轮泵异常分析方法

基于模糊聚类和LSTM网络，提出了一种数据驱动的运载火箭发动机氧涡轮泵数据异常分析方法。通过模糊聚类对工况复杂，标签不完整的数据样本进行预分类，得到完整的标签并且分析特征贡献度，为LSTM网络的特征筛选和训练打下基础；通过LSTM网络对氧涡轮泵数据进行预测，并计算预测结果与原始数据之间的平均误差，再根据非参数阈值计算方法计算的阈值判据来判断设备是否异常，最终实现了氧涡轮泵数据驱动的故障检测报警，相较于红线阈值检测方法准确率提升7%。     

舰载直升机系统安全评价指标体系

为了准确地对舰载机系统进行安全分析，确定客观的安全评价指标，文章选择“人、机、环境、管理”4个方面共29个评价指标，这些指标基本涵盖了舰载机系统的安全评价所包含的有效信息。利用模糊聚类的方法再对该指标体系进行筛选，最终确定了精简且具有科学性的指标体系。