机器视觉在军事领域的应用现状及发展趋势

机器视觉技术凭借其非接触测量、实时性好、可持续工作等优点，在军事领域中有着广阔的应用前景。在对机器视觉光学照明系统、成像系统、视觉信息处理系统等关键技术进行概述的基础上，详细分析了机器视觉技术在军事领域进行典型目标物识别、人员识别、装备缺陷检测等典型场景以及典型军事装备上的应用现状。在此基础上，指出了机器视觉在军事领域的应用，仍然存在视觉传感器硬件系统难以适应极端环境、复杂的军事目标适应性不足、目标识别的实时性难以保证、多传感器融合获取军事目标信息能力缺乏等问题。同时，对机器视觉技术在军事领域应用的未来发展趋势进行了展望，研究分析结果可为机器视觉在军事领域的进一步实用化提供参考。     

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.     

基于极化神经网络的雷达舰船检测识别方法

相参雷达捕获的全极化海面目标距离-多普勒(RD)回波数据中，目标区域占比小、信噪比低，且海况环境与干扰种类多变，使得经典的深度神经网络在此种条件下检测识别精度较低。为此，本文提出了一种基于极化深度神经网络的全极化相参雷达海面目标检测识别算法。首先，引入极化特征提取模块挖掘目标与干扰的差异化特征;其次，通过特征金字塔网络解决小目标检测识别的问题;最后，使用级联结构进一步提升算法性能。在全极化相参雷达回波数据集上的测试结果表明:基于特征值与特征矢量的极化特征对于数据集中两类舰船目标的平均精度分别达到0.907 9与1.0，相比不采用极化特征有着显著提高。     

基于改进的卷积神经网络的步态识别

为了提高惯性传感器采集到的序列数据中步态识别的准确率，建立了一个激励层改进的卷积神经网络(CNN)模型。针对三轴加速度传感器对运动太过敏感导致步态周期划分不准确的问题，采用加速度传感器与弯曲度传感器组合获取人体运动信息。将CNN模型中激励层的线性整流函数（ReLU）改进为带泄露线性整流函数(Leaky ReLU)，以解决遇到卷积输出数据小于0时神经元被抑制的问题，进而达到提高步态识别准确率的目的。实验结果表明：激励层优化的CNN模型在行走、上下楼和上下坡五种步态模式下识别率达到了95.79%，与未采用弯曲度传感器的改进CNN模型和未进行激励层改进的CNN模型相比，步态识别率有所提高。     

海面舰船目标雷达距离高分辨特性测量

雷达距离高分辨特性,即目标的一维高分辨距离像特性,能够体现目标的形状及结构信息,且易获取,有利于对目标进行准确识别,这已成为目前雷达目标识别领域的一个重要研究方向。开展目标一维高分辨特性研究的一个重要基础是测量,因而通过测量目标的一维高分辨距离像并分析目标特性,对目标识别具有重大理论意义与实际应用价值。基于此,文章利用实验室现有全相参新体制雷达导引头目标测量平台,对海面舰船目标的距离高分辨特性进行了初步实测,获取了海面舰船的米级高分辨距离像,为今后深入开展海面舰船目标识别研究奠定了坚实基础。     

超视距空战仿真中的策略识别

针对超视距空战仿真中敌机策略的识别问题,研究了一种基于案例的策略识别方法。该方法通过构建包含假定的对手任务目标、观测数据、策略等内容的案例库,采用相似度计算选择与新的观测相似的案例库子集,并计算策略概率分布来识别对手策略。实验证明,相比与常规的基于案例推理方法,在案例中增加敌机任务目标提高了策略识别准确率,并且在假定敌机目标不正确时,能修复错误假定并进行策略识别,改善了基于案例策略识别方法的性能。     

基于FPGA的BOC(1,1)信号捕获技术

BOC调制是新一代卫星导航系统中广泛采用的调制方式,在时域上具有多峰值特性,副峰的存在增加了捕获的难度。采用自相关副峰消除技术(ASPeCT)在原有的BOC码相关支路的基础上增加一条伪码相关支路,可以有效削弱码相位检测函数的副峰,避免误捕获,是一种性能良好的BOC信号捕获技术。提出一种采用二维并行快速搜索的在FPGA芯片中实现ASPeCT捕获技术的方案,可以显著缩短捕获时间。在理论分析和仿真验证的基础上,采用Verilog硬件描述语言在接收机FPGA+DSP平台上编程实现,在露天测试中,成功捕获到Galileo E1B卫星导航信号,并对ChipScope采集的数字中频信号的多普勒频率和码相位静态捕获结果进行验证,与同一段信号的Matlab仿真结果一致,证明所提出的技术方案正确合理可行。     

双偏振干涉式光纤陀螺的多相位调制技术

在双偏振干涉式光纤陀螺的发展过程中，光纤环上的应力、扭转等会造成正交偏振态之间的交叉耦合，降低陀螺系统的稳定性。提出了一种基于双偏振干涉式光纤陀螺的六态方波调制技术并进行了理论推导。该技术与传统的方波和四态方波调制相比，降低了偏振交叉耦合误差，提高了信噪比，大大增加了信号解算精度。通过实验对比测试了干涉式光纤陀螺在不同调制技术下的偏置稳定性。实验结果表明六态方波调制技术的偏置稳定性达到了9.85×10-4（°）/h，验证了六态方波提高信号解算精度的效果。     

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.     

基于深度卷积网络的SAR图像目标检测识别

在SAR图像解译应用领域,目标的自动检测与识别一直是该领域的研究重点和热点,也是该领域的研究难点。针对SAR图像的目标检测与识别方法一般由滤波、分割、特征提取和目标识别等多个相互独立的步骤组成。复杂的流程不仅限制了SAR图像目标检测识别的效率,多步骤处理也使模型的整体优化难以进行,进而制约了目标检测识别的精度。采用近几年在计算机视觉领域表现突出的深度学习方法来处理SAR图像的目标检测识别问题,通过使用CNN、Fast RCNN以及Faster RCNN等模型对MSTAR SAR公开数据集进行目标识别及目标检测实验,验证了卷积神经网络在SAR图像目标识别领域的有效性及高效性,为后续该领域的进一步研究应用奠定了基础。