基于残差修正CNN-BiLSTM的空中目标航迹短期预测算法

针对因深度学习自身局限性和递归预测策略产生的累积误差,导致航迹预测精度不高的问题,提出了一种基于残差修正CNN-BiLSTM的空中目标航迹短期预测算法。首先,引入卷积模块用于提取航迹数据之中具有潜在关联的空间位置特征,利用双向长短时记忆网络提取航迹数据中的时序特征,并实现对空中目标的实时单步预测和多步超前预测;其次,引入整合移动平均自回归为残差修正模型,对实时单步预测产生的残差建模,计算混合神经网络模型多步超前预测时的残差值;最后,将混合神经网络模型和残差修正模型的输出结果进行融合,得到最终的航迹预测值。实验结果表明,该算法大大降低了神经网络因自身局限性产生的误差和因递归策略预测产生的累积误差,能够显著提高空中目标航迹短期预测的精度。

空射诱饵弹对地面防空装备作战使用影响研究

空射诱饵弹(miniature air launched decoy,MALD)可诱骗地面防空雷达开机,消耗防空弹药,降低地面防空装备作战效能,提升空中编队突防能力。在梳理空射诱饵弹发展概况基础上,分析空射诱饵弹目标特性,构建典型作战运用场景,开展MALD对制导雷达探测跟踪性能和拦截效能影响分析,采用理论分析和动态仿真的方法研究了空射诱饵弹实施远距离欺骗、抵近干扰对制导雷达探测跟踪性能的影响,采用排队论方法分析MALD对空中编队突防效能的影响。研究结论可为空射诱饵弹战术运用提供参考。

基于协同滤波轨迹预测的机动目标RTPN拦截制导律

针对当前空中威胁目标拦截的实际需求,结合拦截器本身的机动能力,基于全覆盖协同策略,提出一种协同探测的现实真比例导引律(RTPN)制导拦截方法。所提方法解决了传统RTPN方法未考虑拦截器饱和过载限制及对任意机动目标捕获区域的确定问题。此外,针对拦截过程中对目标运动轨迹测量误差及协同探测数据丢包所引起的数据融合精度和鲁棒性问题,提出一种分布式协同滤波算法;针对数据传输和拦截器本身动力学响应延迟等问题,提出一种航迹预测算法。仿真结果验证所提方法能够有效解决饱和过载下的捕获区域确定及动力学延迟问题,及协同探测数据融合中数据丢包所引起的鲁棒性和精度问题。

多视觉传感器协同弱小目标检测

多视觉传感器协同对空实现全区域覆盖的弱小目标检测,在近距离防空领域中具有重要意义。现有的全区域覆盖方法存在覆盖率低、随机性差等问题,弱小目标检测算法存在模型大、定位及分类准确性低等问题。提出了一种高效的对空全区域覆盖算法和轻量级弱小目标检测算法,通过结合最大面积优先法和最小曼哈顿离法改善存在覆盖死角和随机性差等问题。提出密集通道扩展网络(dense and channel expand network,DCENet)模型,基于轻量级稠密拼接和自适应尺寸通道扩展方法,在弱小目标数据集上获得了比原算法更有竞争力的平均精度结果。

制导炸弹类目标运动建模与仿真

针对现代战争中制导炸弹对要地防空的主要威胁,提高防空武器系统的综合作战效能,结合制导炸弹的受力分析、运动分析,采用了极限参数动力学模型,由航迹控制函数根据制导炸弹的轨迹特性及过载能力限制给出制导炸弹在飞行过程中的受力及控制参数,并对国外典型的制导炸弹弹道进行了模拟仿真验证。验证结果表明,仿真产生的制导炸弹航迹数据比较真实地反映了滑翔类制导炸弹的运动规律,计算简单,实时性好,能够为防空武器对制导炸弹的拦截方式研究提供有效的仿真措施。

反无人机作战方案评估方法研究

反无人机作战中涉及的手段丰富、装备复杂,需考虑的因素众多,致使对防御作战方案的综合评估及优化存在较大难度。鉴此,基于层次分析法开展的作战方案综合评估,在构建目标层、准则层和指标层等三层指标体系的基础上,结合专家调研和打分,构造判断矩阵,全面评估各级指标的权重向量,进而推算出各底层指标相对顶层目标的影响权重。在此基础上,借助军事仿真的手段得出底层指标的模拟值,结合其对应的影响权重,即可在不同作战方案间进行无量纲的对比评估,解决了由可测度较高的指标推导作战方案总体评估结论的难题,同时还可更好地发现对提升作战效能影响较大的因素,以及为高效优化作战方案找出更为现实可行的途径。

基于PREA环的海上编队防空反导棋谱设计

论文针对防空反导过程中大型舰艇编队的防空阵位问题开展研究,分析了传统舰艇编队防空队形的配置方法和局限性。运用PREA环指挥控制机理,重新审视了防空反导的作战全过程,提出了基于PREA环的防空反导棋谱模型设计方法,利用六角格构设了防空反导棋谱,并阐述了棋谱防空的对抗过程,对于指导未来防空反导作战、装备设计有一定指导价值。

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

Anti-swarm UAV radar system based on detection data fusion

There is a growing body of research on the swarm unmanned aerial vehicle(UAV)in recent years,which has the characteristics of small,low speed,and low height as radar target.To confront the swarm UAV,the design of anti-UAV radar system based on multiple input multiple output(MIMO)is put forward,which can elevate the performance of resolution,angle accuracy,high data rate,and tracking flexibility for swarm UAV detection.Target resolution and detection are the core problem in detecting the swarm UAV.The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar.Since MIMO radar has better performance in resolution,swarm UAV detection still has difficulty in target detection.This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect.Subsequently,signal processing and data processing based on the detection fusion algorithm above are designed,forming a high resolution detection loop.Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

基于组合赋权逼近理想解法的岸-海联合防空目标威胁评估

为正确评估敌方来袭目标对岸海联合防空网络的整体威胁,构建来袭目标威胁评估的指标及其隶属度表示,基于群组AHP和改进熵权法给出指标的主客观组合权重。通过逼近理想解法给出来袭目标相对单防御平台和整个防御网络的威胁度。仿真结果表明,所构建威胁评估体系具备有效性。

机载主动防御系统毁伤技术发展现状及趋势

发展机载末端主动防御系统已成为当前战场环境下提高军机生存力的现实途径,毁伤单元作为打击链路的最终作用环节,其有效与否直接关系到整个主动防御系统的作战效能。本文从机载主动防御系统的对抗原理出发,梳理了机载主动防御系统的发展现状,提出构建“软硬协同”对抗式综合主动防御系统的总体设计理念,并从软杀伤和硬杀伤两个方面给出发展主动防御系统毁伤技术的思路与建议。

水面舰艇反无人机技术发展研究

目前水面舰艇面临的主要空中威胁为各种亚音速和超音速的反舰导弹,但随着无人机的快速发展和大量使用,无人机及其携带的精确制导武器正逐步成为水面舰艇新的重要威胁。现有舰载防空手段对无人机拦截效费比低、作战效能差,因此为水面舰艇提供低成本、高效能的无人机反制手段具有重要现实意义。本文对现有无人机装备进行分类总结,分析了水面舰艇面临的潜在无人机威胁;通过调研当前主流反无人机装备的作战能力及应用特点,结合当前无人机水面作战典型样式和水面舰艇防空作战能力,论述了水面舰艇反无人机技术目标探测复合化、拦截手段多样化、火控系统一体化、武器装备模块化以及装备成本最小化的发展趋势;同时,分析了舰艇反无人机涉及的关键技术,以期为舰艇反无人机技术发展提供参考。

基于改进模糊层次分析法的某诱饵效能评估研究

随着防空系统网络化、综合化发展,空中进攻作战面临的威胁越来越大,防空火力压制与摧毁作战意义重大,但风险极高。为此,美国等国家发展了空射诱饵等装备,在20世纪末的几次典型战争中发挥了重要作用。本文以某空射诱饵的效能评估为研究对象,针对传统模糊层次分析法存在的一些不足,在专家调查、比较标度、判断矩阵、权重计算以及模糊隶属矩阵等方面提出了改进措施,进一步提高了评估方法的客观性与科学性。

基于并行作战环的无人机集群协同作战能力评估方法

针对无人机集群基于信息网络的协同作战能力评估问题,采用衡量信息不确定性程度的信息熵对网络的节点能力和作战边的权值进行量化,构建信息流转动态评估网络。首先通过节点度受限最短路算法找出该网络可能的并行作战环,分串行、并行和混合3种模式的计算法算出评估网络最小能力熵,实现无人机集群协同作战行动量化评估;然后通过对无人机集群海上封控作战行动进行能力评估,验证该评估方法的有效性;最后基于最大效能原则,给出无人机集群协同策略和作战使用上的建议。

Accurately tracking hypersonic gliding vehicles via an LEO mega-constellation in relay tracking mode

In order to effectively defend against the threats of the hypersonic gliding vehicles(HGVs),HGVs should be tracked as early as possible,which is beyond the capability of the ground-based radars.Being benefited by the developing megaconstellations in low-Earth orbit,this paper proposes a relay tracking mode to track HGVs to overcome the above problem.The whole tracking mission is composed of several tracking intervals with the same duration.Within each tracking interval,several appropriate satellites are dispatched to track the HGV.Satellites that are planned to take part in the tracking mission are selected by a new derived observability criterion.The tracking performances of the proposed tracking mode and the other two traditional tracking modes,including the stare and track-rate modes,are compared by simulation.The results show that the relay tracking mode can track the whole trajectory of a HGV,while the stare mode can only provide a very short tracking arc.Moreover,the relay tracking mode achieve higher tracking accuracy with fewer attitude controls than the track-rate mode.

Evaluation method and optimization strategies of resilience for air&space defense system of systems based on kill network theory and improved self-information quantity

Resilience of air&space defense system of systems(SoSs)is critical to national air defense security.However,the research on it is still scarce.In this study,the resilience of air&space defense SoSs is firstly defined and the kill network theory is established by combining super network and kill chain theory.Two cases of the SoSs are considered:(a)The kill chains are relatively homogenous;(b)The kill chains are relatively heterogenous.Meanwhile,two capability assessment methods,which are based on the number of kill chains and improved self-information quantity,respectively,are proposed.The improved self-information quantity modeled based on nodes and edges can achieve qualitative and quantitative assessment of the combat capability by using linguistic Pythagorean fuzzy sets.Then,a resilient evaluation index consisting of risk response,survivability,and quick recovery is proposed accordingly.Finally,network models for regional air defense and anti-missile SoSs are established respectively,and the resilience measurement results are verified and analyzed under different attack and recovery strategies,and the optimization strategies are also proposed.The proposed theory and method can meet different demands to evaluate combat capability and optimize resilience of various types of air&space defense and similar SoSs.

Network-based structure optimization method of the anti-aircraft system

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities.Firstly, the thought of combat network model(CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength(CAST) logic and influence network(IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network(TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed.Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-Ⅱ(NSGA2) is used to solve the multiobjective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-Ⅲ(NSGA3)and strength Pareto evolutionary algorithm-Ⅱ(SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

基于ABMS和正交试验的防空作战效能评估及优化

为研究具有多输入、非线性特点的近程防空系统作战效能和优化问题,在基于ABMS理论的Anylogic软件环境中构建了通用近程防空系统作战仿真推演模型,并提出了一种基于自然科学领域常用的正交试验设计的通用近程防空武器系统作战效能评估和优化方法,解决了常规评估方法动态评估能力不足和仿真平台方法运算量大的问题。以平原地区弹炮结合防空系统为例,通过正交试验方法得到的数据训练出效能评估BPNN,并将其作为优化算法的目标函数,通过NSGAⅡ算法得出仿真推演模型的最优输入决策向量,结果与正交试验分析得到的最优解接近一致。仿真试验表明,采用ABMS和正交试验相结合的方法建立的评估体系不仅具备动态评估能力,也可以较快的分析速度得到评估体系的最优决策向量,为防空作战运用提供合理建议。

面向飞行目标的多传感器协同探测资源调度方法

针对飞行目标机动性带来的多传感器协同探测资源调度动态性需求,提出一种新的基于近端策略优化(Proximal policy optimization,PPO)与全连接神经网络结合的多传感器协同探测资源调度算法.首先,分析影响多传感器协同探测资源调度的复杂约束条件,形成评价多传感器协同探测资源调度过程指标;然后,引入马尔科夫决策过程(Markov decision process,MDP)模拟多传感器协同探测资源调度过程,并为提高算法稳定性,将Adam算法与学习率衰减算法结合,控制学习率调整步长;最后,基于改进近端策略优化与全卷积神经网络结合算法求解动态资源调度策略,并通过对比实验表明该算法的优越性.