多目标数据关联的神经网络方法

数据关联是多目标跟踪的关键问题。基于 Hopfield神经网络的 JPDA是解决这一关键问题的有效方法之一 ,但此方法的难点在于优化系数的整定。提出一种改进算法 ,用于解决优化系数在线自适应整定问题。首先重新构造了李雅普诺夫能量函数 ,接着引入变化的优化系数因子 ,并给出了优化系数求解的迭代公式 ;最后对已有和改进的算法进行了仿真研究。结果表明改进的方法和原有的方法相比 ,一方面具有在线整定优化系数的功能 ,另一方面可以获得和原有算法非常接近的估计误差。     

用结构自适应前馈网络解算多目标导弹攻击区

多目标导弹攻击区的实时解算是机载多目标攻击武器系统得以技术实现的中心环节。阐述了“多目标导弹攻击区”的概念 ,探索了其解算的技术途径 ,提出了“实时、高精度和低存储”的解算要求。根据上述技术要求 ,设计了基于网络灵敏度统计分析的多层前馈网络结构自适应算法 ,以用于训练合适规模的多层前馈网络逼近器。在此基础上 ,进行了系统仿真研究 ,研究的结果表明了该方案的应用前景。     

多传感器多目标定位与跟踪技术研究

针对炮兵打击目标的特性和获取目标信息所采用的侦察设施 ,研究了多传感器多目标定位和跟踪问题。在闭圆和聚类概念的基础上 ,提出了一种多传感器多目标融合算法 ,并给出了状态估计的最优解。仿真结果证明了这种算法的有效性。这一模型的物理实现正在进一步研究之中。     

红外成像水面靶标的设计与实现

水面靶标为反舰武器海上飞行试验提供水面模拟舰船目标。介绍了红外成像靶的功能和技术指标，详细给出了舰船红外成像模拟实现的总体设计方案、硬件和软件实现方式。海上测试结果表明，这种靶标能够较好地实现舰船红外热像模拟，在海上试验的实际应用中取得了预期效果。     

An air cavity method for increasing the underwater acoustic targets strength of corner reflector

In order to improve the underwater acoustic target strength of corner reflectors, according to the principle of acoustic impedance mismatch of the boundary layer, the method of using air cavity to increase the underwater acoustic target strength of corner reflectors is proposed. The acoustic reflection coefficients of underwater air layer and single layer metal sheet are calculated and compared. The results show that the reflection coefficient of single layer metal sheet is greatly affected by frequency and incidence angle, and the reflection coefficient of air layer in water is large and little affected by frequency and incidence angle. On this basis, a new kind of airfilled cavity corner reflector is designed. The acoustic scattering characteristics of underwater airfilled cavity corner reflector are calculated cumulatively, and the results are compared with the monolayer metal sheet corner reflector. The simulation results show that the acoustic reflection effect of the airfilled cavity corner reflector is better. In order to verify the correctness of the method, the test was carried out in the silencing tank. The experimental results show that the simulation results are in good agreement with the experimental results, and the airfilled cavity can improve on acoustic reflection performance of the underwater corner reflector.     

防空兵群射击指挥方式及其优化问题

从防空兵作战指挥的实际需求出发,利用概率论和防空作战运筹理论,描述了集中、分散、混合和平行等射击指挥方式的概念、特点及其相互关系,论述了射击指挥方式定量分析的基本方法、主要依据和指标,建立了基于群抗击效率的评估模型,在对多种射击指挥方式进行定量比较的基础上,揭示了射击指挥中具有普遍意义的、潜在的实用规则.     

多约束下的高超声速飞行器三维非线性自适应末制导律

面向多约束下高超声速飞行器末制导过程中的通道耦合、参数扰动、模型失配等突出问题,设计一种适于高超声速飞行器的三维非线性自适应末制导律。为了模型描述的完整性和简洁性,引入视线旋量和旋量速度的概念,并基于此建立三维制导参考模型和实际系统的表达式;为了保证制导律的鲁棒性和自适应性,基于自适应控制理论,设计一种三维非线性自适应制导律;通过数学推导证明了该制导律的稳定性。该制导律能够从理论上克服高超声速飞行器末制导面临的通道耦合、参数扰动、模型失配等突出问题,满足多约束制导要求。仿真结果验证了所设计制导律的有效性。     

反舰导弹对目标信息迟滞的适应性分析

目标信息迟滞(延误)情况下的导弹攻击是导弹超视距攻击的一种常态。以直升机引导反舰导弹实施超视距攻击为例,在全面分析反舰导弹超视距攻击射击误差因素的基础上,建立了反舰导弹捕捉概率模型,对目标信息迟滞对导弹捕捉概率的影响进行了精确定量分析,得出了相关结论,并对导弹超视距攻击的引导与实施提出了建议与要求。     

基于模糊推理的雷达辐射源识别方法

辐射源数据往往不完整或不确定,因此应用模糊推理实现了辐射源和目标平台的识别.将辐射源特征转换为由模糊集及隶属度函数表征的参数,再通过与数据库中的辐射源数据进行模糊匹配来判断辐射源的类型,实现了雷达辐射源的被动识别方法.进一步通过识别出目标平台上所装备的雷达辐射源的参数值,利用模糊推理,推断出目标平台的类型.试验结果表明:模糊推理的算法是切实可行的,在对模拟数据加入的噪声不超过一定限制的情况下,对于数据库已有的目标能够达到较高的目标识别率.     

An infrared target intrusion detection method based on feature fusion and enhancement

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning. In view of the characteristics of intrusion targets as well as inspection difficulties, an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed. This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels. Among them, LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library, while motion frame difference method can detect the moving regions of the image, improve the integrity of target regions such as camouflage, sheltering and deformation. In order to integrate the advantages of the two methods, the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced. The enhancement module of the network strengthened and screened the targets, and realized the background suppression of infrared images. Based on the experiments, the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated, and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets, and it’s detection performance was far better than the comparison algorithm. The experiment results indicated that, compared with traditional infrared target detection methods, the proposed method could detect the infrared invasion target more accurately, and suppress the background noise more effectively.