基于时频域增强和全变差的群目标信号分离

针对低分辨雷达获取的群目标信号的弱时频正交性以及难以分离的问题,在进行时频域增强处理的基础上,提出了一种基于全变差(TV)的群目标信号分离方法。在旋转目标模型的基础上,首先通过分析群目标信号的稀疏性,指出了进行时频域增强处理的必要性。然后利用群目标中各子目标对应的微动周期的差异性,通过双向延迟处理,对多次观测得到的群目标信号进行时频域增强处理。最后根据群目标信号能量区域的分布特性,利用局部TV融合和主分量分析相结合的方法,实现了群目标信号的高保真分离。仿真结果表明,在采样率较低的情况下,文中方法有效地解决了群目标信号中弱信号分量的分离及提取问题,其融合分辨效果明显优于基于TV范数的融合方法。      

两电极等离子体高能合成射流流场及其冲量实验研究

两电极等离子体高能合成射流激励器通过腔体内电极间的瞬时电弧放电加热腔内气体,在激励器出口产生压差并喷出高速射流,从而产生反作用力和冲量。针对两电极等离子体高能合成射流响应快、持续时间短的特点,设计了单丝扭摆式微冲量测量系统,并结合高速阴影系统,对两电极等离子体高能合成射流的流场发展过程及其单脉冲冲量特性进行了实验研究。实验结果表明,两电极等离子体高能合成射流响应时间小于10μs,射流持续时间约为1ms,射流前锋最大速度约为190m/s,射流流场发展过程中存在多道强压缩波,并以当地声速向下游传播。单丝扭摆式微冲量测量系统可实现μN·s量级冲量测量精度,单脉冲冲量约为32μN·s,并且在低频状态下射流总冲量随激励器放电频率成线性增加。     

大前置角拦截攻击时间控制导引律

为了实现大前置角拦截下的时间一致性饱和攻击,利用非线性导引方程,采取基于预测命中点（PIP）的剩余时间估计方法,结合等效滑模控制理论和Lyapunov稳定性定理,设计了一种大前置角拦截攻击时间控制导引律（ITCG）。针对固定目标和非机动运动目标,在弹目接近速度为负的情况下也能保证准确命中,实现了任意初始前置角下的指定时间到达,拓宽了导弹的制导初始条件,并给出了严格的理论证明。不同初始条件下的仿真结果验证了导引律的有效性。      

无人机集群编队控制演示验证系统

为验证无人机集群编队控制算法在实际环境中的有效性,基于四旋翼无人机平台和双数据链、双地面站冗余设计,搭建了分布式控制的无人机集群编队控制演示验证系统。基于分层控制和封装的思想,将无人机控制系统分为执行层和决策层。执行层采用PIX自驾仪进行封装,只需修改自驾仪参数,不需针对不同无人机平台开发相应的控制策略,就能实现对异构集群的控制。需要验证不同的编队控制算法时,只需对决策层的控制算法进行修改即可,使系统具有较强的适应性和扩展性。演示验证系统采用双地面站和数据链,可实现在多种网络拓扑或通信失败情况下的无人机集群控制,具有较高的稳定性和安全性。应用领导-跟随协同编队控制算法,验证了本文演示验证系统的功能和性能。      

基于UR-MTPGERT网络模型的复杂装备风险传导分析

针对复杂装备风险传导关系描述不清晰的问题,构建了风险传导不确定随机多传递参量图形评审技术（UR-MTPGERT）网络模型。首先,基于机会理论,定义了不确定随机变量的矩母函数,并在此基础上构建了UR-MTPGERT网络模型。其次,为刻画复杂装备的微观风险信息,在模型中引入系统风险度、风险元重要度、风险路径关联度等解析参数。然后,求解矩母函数时,采用德尔菲法处理专家经验数据,得到经验不确定分布,并利用极大熵模型处理随机数据,得到概率密度函数;引入矩阵分析技术,解决了网络拓扑分析困难的问题,在此基础上计算网络参数。最后,对某型飞机进行安全性分析,结果表明,该模型能清晰反映风险元间的关系,可以为复杂装备的风险分析、预判和安全控制提供借鉴。      

输入受限的高超声速飞行器鲁棒反演控制

针对存在参数摄动的吸气式高超声速飞行器（AHV）弹性体模型,考虑执行机构幅值和速率受限问题,提出了一种基于辅助误差补偿策略的鲁棒反演控制方法。采用改进的辅助系统,保证了执行机构在幅值与速率同时达到饱和时闭环控制系统的稳定性和跟踪误差的有界性;采用有限时间收敛微分器（FD）,实现了对虚拟指令及其一阶导数的有效估计,并在此基础上,设计非线性干扰观测器（NDO）,对模型不确定项进行了平滑估计,进一步提高了控制精度。通过仿真,验证了所设计控制方法的有效性。     

基于熵理论的复杂装备费用测算模型

为了提高航空器、飞机等大型复杂装备的费用预测精度，根据相似信息优先原理和熵理论，将相似装备的选取看作是一个信息融合的过程，引入距离熵和灰关联熵，构建综合相似度指标来度量装备样本与待预测装备之间的相似程度，对不同样本进行赋权，建立加权最小二乘法对装备费用进行预测。针对装备样本数量小于参数数量的情形，通过构建装备参数对费用的驱动效应矩阵及计算相应熵权，选择熵权较大的参数作为测算模型的自变量。通过实例对比分析，表明基于熵理论的加权回归测算模型具有较高的预测精度和稳定性。      

自适应变异麻雀搜索优化算法

针对麻雀搜索算法前期易陷入局部极值点、后期寻优精度不高等问题，提出一种自适应变异麻雀搜索算法（AMSSA）。先通过猫映射混沌序列初始化种群，增强初始种群的随机性、遍历性，提高算法的全局搜索能力；再引入柯西变异和Tent混沌扰动，拓展局部搜索能力，使陷入局部极值点的个体跳出限制继续搜索；最后，提出探索者-跟随者数量自适应调整策略，利用各阶段探索者和跟随者数量的改变增强算法前期的全局搜索能力和后期的局部深度挖掘能力，提高算法的寻优精度。选取16个基准函数和Wilcoxon检验进行验证，实验结果表明：所提算法与其他算法相比，寻优精度、收敛速度和稳定性都取得较大提升。     

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy.     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.