Synchronized perturbation elimination and DOA estimation via signal selection mechanism and parallel deep capsule networks in multipath environment

State-of-the-art model-driven Direction-Of-Arrival (DOA) estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model. In this paper, we introduce a framework, based on deep learning, for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment. Firstly, a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method. Then, we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks. The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions, e.g., lower SNRs, smaller snapshots, unknown reflection coefficients and perturbational steering vectors, which make up for the defects of the previous model-driven methods. Simulations are carried out to demonstrate the superiority of the proposed method.     

Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30% of their flight time, and it always exceeds 50% for short-haul aircraft when the ground traffic is congested. Aircraft also contribute significantly to emissions, fuel burn, and noise when taxiing on the ground at airports. There is an urgent need to reduce aircraft taxiing time on the ground. However, it is too expensive for airports and aircraft carriers to build and maintain more runways, and it is space-limited to tow the aircraft fast using tractors. Autonomous drive capability is currently the best solution for aircraft, which can save the maneuver time for aircraft. An idea is proposed that the wheels are driven by APU-powered (auxiliary power unit) motors, APU is working on its efficient point; consequently, the emissions, fuel burn, and noise will be reduced significantly. For Front-wheel drive aircraft, the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn. Forward traction effects the aircraft’s maximum turning ability, which is difficult to be modeled to guide the controller design. Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models; however, the models of related works are always simplified, fixed, or not easily modified, but that is what we care about most. Only with complex models can the trained controller be intelligent. High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design. This paper focuses on the maneuvering problem of front-wheel drive aircraft, a high-fidelity aircraft taxiing dynamic model is established, including the 6-DOF airframe, landing gears, and nonlinear tire force model. A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft. It is proved that in some conditions, the DRL based controller outperformed conventional look-ahead controllers.     

航天器充放电在轨探测试验的发展

介绍了航天器充放电中表面充电和深层介质充电的产生原因，以及充放电试验的发展。分析了国外瞬态脉冲监测仪（TPM）、充电电气效应分析仪（CEEA）、IDM装置、PASP Plus试验、集约环境异常传感器（CEASE）、DDE试验，以厦悬浮电位探测单元（FPMU）等充放电试验的原理厦其试验结果。最后，根据国外的经验和发展趋势，对国内航天器充放电在轨测试的目的、发展步骤，以及与各种空间物理量的关系等提出了相应的建议。     

关于一人公司若干法律问题研究

一人公司作为一种企业发展的有效模式,已经为世界上众多的国家所承认。而对一人公司在我国的设立,法学界一直存在着不同的观点,近期新《公司法》的出台更使这一争论变得激烈。本文针对一人公司的多个法律问题展开探讨,以期对引导和规范我国一人公司的良性发展有所裨益。     

空间深空探测低温制冷技术的发展

深空探测技术已经被列入国家发展计划,由于空间环境的特殊性,对空间低温制冷技术提出了更高的要求。文章介绍了美国、日本和欧空局用于深空探测的深低温制冷技术的发展状况,包括超流氦制冷技术、多级机械制冷技术、吸附式制冷技术、绝热去磁制冷技术和氦稀释制冷技术,并介绍了在天文卫星和空间望远镜上的应用;综述了中国空间低温制冷技术的发展现状,提出了中国开展深低温制冷技术研究的启示和建议。     

一种星载GNSS-R海面风向反演方法

针对目前海面风场反演中风向反演困难,准确性低的问题,提出一种利用深度网络反演海面风向的新方法,该方法采用星载GNSS-R信号作为遥感源,根据海面反射信号特性与风向相关关系选择反演观测量,建立海面风向反演的深度网络模型。引入ReLU函数作为网络激活函数,并基于遗传算法(GA)优化了网络模型,对输入数据进行时空匹配与归一化处理,通过机器学习建立风向与输入变量的对应关系,利用深度网络模型反演得到海面风向,该方法反演结果的均方根误差εRMSE=18.14°,满足海面风向测量精度均方误差小于20°的一般测量要求,并通过两组对比试验验证了该方法的有效性和鲁棒性。     

地球同步轨道目标物深度学习检测方法

针对欧空局SpotGEO竞赛中地球同步轨道目标物的检测问题,提出面向低精度CCD空间图像的深度学习检测方法。在图像预处理环节,分别采用高斯过程回归和模板匹配实现前景/背景分割和多帧图像配准。根据地球同步轨道物体的运动特征,采用拓扑扫描提取候选目标物。在此基础上,提出一套基于深度学习的目标物筛选方法。该方法利用卷积神经网络,依次对拓扑扫描前后候选目标物进行筛选,显著减少噪声点数量,提高检测效率。仿真结果表明,该方法达到98%的目标检测准确率,适用于存在光污染、云层遮挡等干扰的复杂环境。     

基于态势评估及DDPG算法的一对一空战格斗控制方法

已有的空中格斗控制方法未综合考虑基于专家知识的态势评估及通过连续性速度变化控制空战格斗的问题。基于深度确定性策略梯度（DDPG）强化学习算法,在态势评估函数作为强化学习奖励函数的基础上,设计综合考虑飞行高度上下限、飞行过载以及飞行速度上下限的强化学习环境;通过全连接的载机速度控制网络与环境奖励网络,实现DDPG算法与学习环境的交互,并根据高度与速度异常、被导弹锁定时间以及格斗时间设计空战格斗结束条件;通过模拟一对一空战格斗,对该格斗控制方法在环境限制学习、态势评估得分以及格斗模式学习进行验证。结果表明：本文提出的空战格斗控制方法有效,能够为自主空战格斗进一步发展提供指导。     

基于交互融合的高精度图像融合算法的研究

针对可见光和SAR图像融合,提出了一种基于跨模态差分感知和注意力机制的交互融合（TDPAM Fusion）算法,能有效保留可见光图像中的纹理结构和SAR图像的细节信息。首先,采用跨模态差分感知融合（Cross-Modal Differential Perception Fusion, CMDAF）模块提取图像的互补信息,避免真值缺失并提高融合精度。其次,通过坐标注意力机制（Coordinate Attention,CA）提高特征提取的准确性和效率,增强语义信息的集成。最后通过交互融合算法（Interactive Fusion Module,IFM）将特征自适应融合。设计了相应的大型基准数据集,用于网络模型的训练和测试。实验结果表明：TDPAM Fusion融合算法可以获得包含清晰SAR信息的高质量可见光图像。此外,融合算法将互信息（Mutual Information,MI）、空间频率（Spatial Frequency,SF）、视觉保真度（Visual Fidelity,VIF）和相关系数（Correlation Coefficient,CC）等关键指标,分别提高了约6.41%、10...     

基于改进SSD算法的航拍目标检测算法研究

研究基于深度学习技术的无人机航拍图像目标检测算法,首先介绍目标检测算法SSD(Single Shot MultiBox Detector),并对其特征提取网络进行改进,采用稠密特征提取网络替换原网络的主干特征提取网络,提高算法的特征提取能力,从而提升了算法的检测精度。针对网络实时性问题,在算法中引入分组卷积,极大地减少了网络参数量,提升了网络推理速度。为解决训练中出现的正负样本不均衡问题,利用焦点损失（Focal Loss）改进了原算法的损失函数,进一步提升了网络的收敛速度和精度。最后,通过仿真验证了改进算法在目标检测精度上的优越性。