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...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。展开更多
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...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。展开更多
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...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.展开更多
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...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.展开更多
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 p...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.展开更多
文摘空射诱饵弹(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。
文摘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。
文摘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.
基金supported by the Municipal Gavemment of Quzhou(2022D0009,2022D013,2022D033)the Science and Technology Project of Sichuan Province(2023YFG0176)。
文摘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.
基金supported by National Natural Science Foundation of China(Grant No.62073256)the Shaanxi Provincial Science and Technology Department(Grant No.2023-YBGY-342).
文摘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.
