硅基太赫兹频率源关键技术研究进展综述

随着太赫兹技术的发展,由于太赫兹THz(Terahertz)频段具有丰富的频谱资源和相对宽的带宽优势,因此太赫兹频率源表现出广泛的应用前景,如超高速短距无线/有线通信、雷达、医疗、成像和遥感传感器等.本文总结了硅基太赫兹频率源的最新研究成果和存在的问题,概述了近二十年来太赫兹倍频器、太赫兹振荡器、太赫兹天线、太赫兹辐射...     

Preparation of CF/Ni-Fe/CNT/silicone layered rubber for aircraft sealing and electromagnetic interference shielding applications

To meet the needs of preventing information leakage in space engineering and military industry, an efficient method was introduced to obtain layered electromagnetic shielding sealing rubber. The carbon fiber, Ni-Fe coating, and Carbon NanoTube (CNT) were combined by chemical plating and in-situ polymerization to obtain a lightweight (0.14 g/cm²) and thin (1 mm thick) Carbon fiber Fabric (CF)/Ni-Fe/CNT/silicone layered electromagnetic shielding composites. The layered material obtained by adjusting the electroless plating time exhibited a high Shielding Efficiency (SE) of 60.2–85.5 dB in the range of 0–4800 MHz, which can be used for aviation electromagnetic shielding. Carbon fibers and carbon nanotubes mainly attenuated electromagnetic waves through absorption loss, while the nickel-iron alloy coating through reflection loss interacted with the magnetic vector of incident ElectroMagnetic (EM) radiation and magnetic dipoles, therefore, the EM Interference (EMI) shielding composite attenuated EM waves with the “absorption-reflection-absorption” cooperative interaction. Moreover, the flexible fabric substrate adhered with silicone rubber possessed a breaking elongation of 52.3%, which can be utilized as a good sealing material. Simultaneously, the outstanding exothermicity (67.2 °C under the applied voltage of 5 V) makes it possible to be applied in electric heating area. The electromagnetic shielding composites prepared in this paper have good potential in the fields of precision electronic equipment and aviation systems.     

Aerial-BiSeNet: A real-time semantic segmentation network for high resolution aerial imagery

The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes. However, accurate and fast semantic segmentation of high-resolution aerial images for remote sensing applications is still facing three challenges: the requirements for limited processing resources and low-latency operations based on aerial platforms, the balance between high accuracy and real-time efficiency for model performance, and the confusing objects with large intra-class variations and small inter-class differences in high-resolution aerial images. To address these issues, a lightweight and dual-path deep convolutional architecture, namely Aerial Bilateral Segmentation Network (Aerial-BiSeNet), is proposed to perform real-time segmentation on high-resolution aerial images with favorable accuracy. Specifically, inspired by the receptive field concept in human visual systems, Receptive Field Module (RFM) is proposed to encode rich multi-scale contextual information. Based on channel attention mechanism, two novel modules, called Feature Attention Module (FAM) and Channel Attention based Feature Fusion Module (CAFFM) respectively, are proposed to refine and combine features effectively to boost the model performance. Aerial-BiSeNet is evaluated on the Potsdam and Vaihingen datasets, where leading performance is reported compared with other state-of-the-art models, in terms of both accuracy and efficiency.     

Full blended blade and endwall design of a compressor cascade

In the current state-of-the-art, high-loss flow in the endwall significantly influences compressor performance. Therefore, the control of endwall corner separation in compressor blade rows is important to consider. Based on the previous research of the Blended Blade and EndWall (BBEW) technique, which can significantly reduce corner separation, in combination with a non-axisymmetric endwall, the full-BBEW technique is proposed in this study to further reduce the separation in endwall region. The principle of the unchanged axial passage area is considered to derive the geometric method for this technique. Three models are further classified based on different geometric characteristics of this technique: the BBEW model, Inclining-Only EndWall (IOEW) model, and full-BBEW model. The most effective design of each model is then found by performing several optimizations at the design point and related numerical investigations over the entire operational conditions. Compared with the prototype, the total pressure loss coefficient decreases by 7%–9% in the optimized full-BBEW at the design point. Moreover, the aerodynamic blockage coefficient over the entire operational range decreases more than the other models, which shows its positive effect for diffusion. This approach has a larger decrease at negative incidence angles where the intersection of the boundary layer plays an important role in corner separation. The analysis shows that the blended blade profile enlarges the dihedral angle and creates a span-wise pressure gradient to move low momentum fluid towards the mainstream. Furthermore, the inclining hub geometry accelerates the accumulated flow in the corner downstream by increasing the pressure gradient. Overall, though losses in the mainstream grow, especially for large incidences, the full-BBEW technique effectively reduces the separation in corners.     

面向绕月交会对接的同波束VLBI研究

针对"嫦娥5号"(CE-5)探测器间高精度相对测量需求,设计了我国深空干涉测量处理中心框架下的同波束VLBI处理算法,分析了X波段同波束VLBI相位解模糊条件和结果;通过引入群时延辅助的相位干涉技术,大幅抑制了干涉时延随机误差,为同波束VLBI中相位解模糊提供了先验条件;利用CE-5对接实测数据验证了本文工作的有效性,为CE-5任务同波束VLBI的实施奠定了基础。     

板式空心角锥棱镜的研制

在远距离测量、特别是距离达到一公里左右时,角锥棱镜几乎是唯一可行的反射器件。远距离测量的需求是增大角锥棱镜的通光口径,以便于捕捉目标,并增大反射光的能量。常用的整体式角锥棱镜,以及国外的柱式空心角锥棱镜,随着通光口径的增大,都会显著增加棱镜的体积与重量,造成使用不便与工艺困难。解决办法是采用板式结构,经试验,通光口径为80mm的板式空心角锥棱镜,工作距离可达一公里以上,出射光对入射光的平行度达角秒级,体积与重量在可以接受的范围内,而且可以降低工艺难度。     

基于自适应迭代学习的小行星绕飞容错控制

对于小行星绕飞任务的探测器姿态控制问题,已有方法大都考虑了干扰力矩和参数不确定等因素,而忽视了执行器故障情况。针对执行器故障条件下的小行星探测器姿态控制问题,提出了一种基于自适应迭代学习的容错控制方法。所设计的控制器包括两部分：其一针对执行器故障,设计了自适应迭代学习控制器,采用类滑模的思想和自适应迭代学习算法对控制器参数进行调整,进而补偿执行器故障带来的影响,保证系统在控制输出不足情况下的高精度姿态稳定性;其二针对探测器参量变化、外部环境干扰等不确定情况,设计了基于自适应神经网络的迭代学习控制器,采用径向基函数（RadialBasisFunction,RBF）神经网络对系统非线性部分进行逼近,同时对控制器参数进行自适应迭代学习调整,进而保证系统在不确定情况下的动态性能。数值仿真结果表明该控制器能够有效抑制外部环境干扰和内部参数变化带来的不利影响,在执行器部分失效甚至完全失效故障情况下,仍能保证系统的鲁棒性并实现误差在10^-2数量级内的较高姿态控制精度。     

基于人机合作的无人机实时航迹规划

针对复杂多变的战场环境,无人机(UAV)在执行任务遇到突发威胁时,提出了一种人机合作的实时航迹规划方法。由人决策和分析威胁信息,给出规避方向和任务紧急程度,无人机据此采用模糊推理的方法,自主解算得到引导点的位置,牵引无人机改变航向,规避突发威胁。仿真结果表明,采用人机合作的实时航迹规划可以将人的智能决策和无人机的快速计算能力有机结合起来,规划更加优化的航迹,能动态调整引导点的位置,可根据任务的紧急程度灵活选择规避路径。     

高动态GPS信号粗捕和精捕算法仿真实现

为了解决高动态下对GPS信号快速捕获的问题,提出了改进的部分匹配滤波(PMF)和快速傅里叶变换(FFT)相结合的粗捕方法,对其原理和结构进行了分析,并针对其引起的扇贝损失和捕获性能不高的问题,对传统PMF+FFT方法进行加窗处理;考虑到高动态下基于扩展卡尔曼跟踪的方法对捕获后参数的精度要求很高,因此在粗捕的基础上提出了基于线性调频Z变换(CZT)算法的精捕方法,并在GPS信号理论模型和模拟高动态轨迹的基础上,实现了高动态GPS数字中频信号的生成,为进一步加快捕获速度,对于冷启动时提出了一种组合码相关的卫星快速盲搜方法;最后通过MATLAB进行系统仿真实验,验证了所提出的高动态GPS信号粗捕和精捕算法能在加速度为100 g的高动态环境下有约10 Hz的捕获精度。     

基于ANP的直升机单机森林灭火训练效能评估

现有的直升机单机森林灭火训练效能评估方法仅针对取水灭火过程,忽视了其他任务环节对训练效能的贡献,并假设指标间相互独立,因此导致评估指标体系不全面、指标权重分配不合理,评估结果的可靠性低。利用离散事件系统建模理论建立的事件活动流将直升机单机森林灭火训练任务分解成若干相关活动,活动映射法对各活动效能要素进行分析并转化为效能评估指标,最终构建出全面的两维度效能评估指标体系;网络分析法(ANP)的使用考虑了评估指标间具有的依赖和相关关系,对指标权重进行重新合理分配,评估方法更符合问题的实际情况,评估结果更加可信。基于超级决策(SD)软件的案例分析表明,所提出的效能评估方法具有工程应用的可行性和参考价值。