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。     

On the time lag between solar wind dynamic parameters and solar activity UV proxies

The solar activity displays variability and periodic behaviours over a wide range of timescales, with the presence of a most prominent cycle with a mean length of 11 years. Such variability is transported within the heliosphere by solar wind, radiation and other processes, affecting the properties of the interplanetary medium. The presence of solar activity–related periodicities is well visible in different solar wind and geomagnetic indices, although their time lags with respect to the solar cycle lead to hysteresis cycles. Here, we investigate the time lag behaviour between a physical proxy of the solar activity, the Ca II K index, and two solar wind parameters (speed and dynamic pressure), studying how their pairwise relative lags vary over almost five solar cycles. We find that the lag between Ca II K index and solar wind speed is not constant over the whole time interval investigated, with values ranging from 6 years to $～$1 year (average 3.2 years). A similar behaviour is found also for the solar wind dynamic pressure. Then, by using a Lomb-Scargle periodogram analysis we obtain a 10.21-year mean periodicity for the speed and 10.30-year for the dynamic pressure. We speculate that the different periodicities of the solar wind parameters with respect to the solar 11-year cycle may be related to the overall observed temporal evolution of the time lags. Finally, by accounting for them, we obtain empirical relations that link the amplitude of the Ca II K index to the two solar wind parameters.     

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.     

Local empirical model of ionospheric variability

The paper presents an analysis of the ionospheric variability as a function of local time, month, and geomagnetic activity level. The 2003–2020 dataset of peak electron densities (NmF2) from the Irkutsk DPS-4 Digisonde (52.3°N, 104.3°E) was converted into the dataset of the NmF2 disturbances (ΔNmF2) representing the relative (percentage) deviations of the NmF2 from the 27-day running median. The ΔNmF2 dataset was used to calculate root mean square values of ΔNmF2 (σNmF2) by 27-day running averaging. These σNmF2 values were considered as a measure of ionospheric variability. The σNmF2 as function of local time, day of year, and year was the input for building the local empirical model of ionospheric variability based on the linear regression of σNmF2 on the 27-day average daily Ap-index of geomagnetic activity. The paper demonstrates the diurnal-seasonal variations in σNmF2 under low geomagnetic activity (linear regression intercept) as well as the rate of increase/decrease in σNmF2 with increasing Ap (linear regression slope). The obtained diurnal, seasonal, and geomagnetic activity behavior of σNmF2 is compared with previous studies of ionospheric variability.     

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。     

智能博弈趋势下未来空天防御体系展望

当前，军事智能化正成为继机械化、信息化之后推动新一轮军事变革的强大动力，深刻影响着未来战争的制胜机理、作战规则及战争形态。本文全面分析了智能化在未来空天防御体系的应用优势，结合国外智能化防御体系发展特点，提出了未来智能空天防御体系发展设想，梳理了空天防御体系智能化关键技术，阐述了空天防御体系智能化变革的重要意义。     

一种针对多光谱遥感图像的桥梁快速识别算法

提出了一种针对多光谱图像中桥梁的识别算法。首先,根据水体和背景地物在不同光谱波段的亮度差异,计算多光谱图像的水体指数得到水体增强图,搜索其具有明显双峰的直方图得到最优阈值,实现河流的完整提取；其次,利用桥梁的存在会导致局部水体的光谱异常,沿河流中间线进行潜在桥梁区域的快速提取；再进一步利用桥梁长度以及与河流的空间关系进行鉴别,有效剔除虚警。利用 SPOT4遥感影像进行实验,结果表明本文算法运算量小,对于多个桥梁的识别具有很好的实用性。     

基于LOFAR谱图的水下目标识别方法

基于LOFAR谱图的主成分分析法是目前用于对水下目标识别的重要方法之一,文中详细分析了该方法的基本原理和具体应用过程,并对潜艇辐射噪声的实测数据进行了仿真和分类试验,给出了仿真图和试验结果.结果表明,运用该方法对水下目标进行识别的识别率较高.     

基于可重构标定板的激光与视觉联合标定方法

目前,激光雷达与视觉传感器的联合标定方法包含动态在线标定与静态离线标定两大类.动态在线标定对标定的环境有较高的要求,且标定结果不稳定;静态离线标定通常采用标定板,因而对标定板的要求较高,手动选择与线拟合都易引入或放大误差,故提出了一种基于可重构标定板的激光雷达与视觉传感器的联合标定方法.首先,对传统的标定板进行拆分与重...