太空目标监视装备作战试验设计研究

按照现行装备试验鉴定体制的要求,装备作战试验是新研太空目标监视装备在正式交付使用单位前必须完成的一类试验。太空目标监视装备具有精密昂贵、价值高、单台套、集成度和复杂度高等特点,开展作战试验与普通的侦察探测装备和常规兵器区别较大。本文介绍了太空目标监视装备作战试验的对象与要求,分解了太空目标搜索发现、监视管理、目标识别、信息共享等实战能力和实战能力的生成与保持要求,构建了作战试验评估指标体系,并依据太空目标监视任务剖面和环境剖面设计了作战试验项目,基于作战试验指标考核评估确定了评估数据采集需求,紧贴太空目标监视装备实际使用面临的环境条件构设了逼真的作战试验环境条件。这些研究可为太空目标监视装备作战试验实践提供支持。     

欧盟太空安全政策与发展动向

欧盟具有相对成熟的太空技术,处于国际太空力量发展的第一梯队,其太空安全政策是在基于维护太空自主权的前提下,寻求太空与安全防务政策的统一,从而实现共同外交和安全政策的整体目标,目前已取得了较大进展。本文认为,从未来发展来看,面对有限的太空资源优势和脆弱的太空环境,欧盟将会继续利用其技术优势和集体力量,努力保障其太空关键基础设施安全和太空自主权,同时进一步将太空安全重点向军事竞争领域聚焦,将其作为拓展战场空间维度的重要途径。     

俄罗斯太空安全政策探析

后苏联时代的俄罗斯未能维持其在太空安全领域的优势,已不再是太空军事强国。本文首先梳理了俄罗斯太空安全政策的发展历程,其次,讨论了俄罗斯在太空安全治理和防止太空武器化方面的外交倡议,最后总结了俄罗斯太空安全政策的特征。本研究认为,自21世纪初起,俄罗斯逐渐开始复兴其太空军事力量,力图使其太空安全政策适应新的地缘政治格局,不仅积极调整其航天计划,对其航天工业体系、航天机构和航天资产进行简化、整合和现代化建设,还谋求“非对称”反制策略,并在联合国框架内积极开展“太空外交倡议”,以应对美国太空武器化所带来的威胁和挑战,同时推动禁止太空武器化进程。     

美军太空信息支援战区作战研究

为了保证太空信息能自顶向下融入到战区联合作战行动,实现太空信息支援力量与其他作战部队的跨域协同,美军建立了体系健全、功能完备的太空信息支援系统。本文梳理归纳了美军太空信息支援力量体系,针对太空信息支援战区作战的指挥与运用流程,分析了“统分结合、视情授权”的太空信息支援战区作战指挥体系,研究了“需求提出－汇总分析－分类提供”的支援作战流程,对天基侦察监视、卫星通信中继、卫星导航定位的需求管理、任务规划与资源调度的具体运用流程进行了分析,总结了美军太空信息支援战区作战指挥控制的特点。希冀本研究可为太空信息支援作战的指挥体系构建和指挥控制流程设计提供一定参考。     

Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is compara-tively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R-CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R-CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the imple-mentation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.     

Visual-attention gabor filter based online multi-armored target tracking

The multi-armored target tracking (MATT) plays a crucial role in coordinated tracking and strike. The occlusion and insertion among targets and target scale variation is the key problems in MATT. Most state-of-the-art multi-object tracking (MOT) works adopt the tracking-by-detection strategy, which rely on compute-intensive sliding window or anchoring scheme in detection module and neglect the target scale variation in tracking module. In this work, we proposed a more efficient and effective spatial-temporal attention scheme to track multi-armored target in the ground battlefield. By simulating the structure of the retina, a novel visual-attention Gabor filter branch is proposed to enhance detection. By introducing temporal information, some online learned target-specific Convolutional Neural Networks (CNNs) are adopted to address occlusion. More importantly, we built a MOT dataset for armored targets, called Armored Target Tracking dataset (ATTD), based on which several comparable experiments with state-of-the-art methods are conducted. Experimental results show that the proposed method achieves outstanding tracking performance and meets the actual application requirements.     

A novel DOA estimation algorithm using directional antennas in cylindrical conformal arrays

In this paper, a novel direction of arrival (DOA) estimation algorithm using directional antennas in cy-lindrical conformal arrays (CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix (CCM) of the sub-arrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays, and the unitary estimation of the signal parameters via rotational invariance techniques (ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio (SNR) environment.     

多传感器多机动目标跟踪方法研究进展

讨论了多传感器多机动目标的研究现状,分析了几种跟踪滤波器的性能,着重探讨了交互多模型(IMM)算法的发展状况和应用。同时还研究了多传感器跟踪多机动目标过程中的数据关联方法。最后指出,寻求采用自适应变结构的IMM算法,研究模型切换过程中算法的过渡性能,以及在数据关联中引入混沌、遗传算法等智能控制方法,将是未来多传感器多机动目标研究的方向。     

基于系统动力学的导弹体系作战效能仿真研究

简要介绍了系统动力学的概念;分析了导弹作战体系的构成以及各子系统对体系的贡献效能;针对导弹体系对抗作战效能仿真,运用系统动力学方法,建立了导弹体系对抗的系统动力学模型;并对在不同对策条件下的仿真结果进行了分析,为导弹体系结构优化、体系能力的提高提供了依据。     

防空、反潜作战中舰载设备生存能力的定量评估算法

防空、反潜作战是当前海上编队面临的最重要的作战样式之一,研究舰载设备在海战中的生存能力,对于提高水面舰艇可持续作战能力具有重要作用。采用定量评估方法来研究舰载设备的生存能力问题,基于水面舰艇编队典型防空、反潜作战防线模型,给出了详细的数学计算模型。