一种星-箭连接动态界面力的深度学习反演方法

针对于星-箭连接动态界面力无法通过力传感器直接测量,且典型时域动载反演方法难以准确计算界面力的时域变化等难点,提出了基于长短时记忆(LSTM)神经网络的星-箭界面力深度学习反演方法。首先通过卫星地面测试试验得到数据依据,以卫星主体结构的加速度测量数据为输入层,以星-箭界面力测量数据为输出层,利用LSTM神经网络建立输入和输出间的反演映射关系模型,实现卫星在发射过程中较高精度的界面力反演。进而,设计并开展了某典型卫星结构的正弦扫频和随机振动实验,测试LSTM界面力反演方法的可行性。结果分析可知,所提出的基于LSTM深度学习反演方法能够精确地获得动态界面力时程数据,两项性能指标均优于目前典型的载荷反演方法。     

改进PointNetLK的点云智能配准与位姿图优化方法

针对空间在轨服务任务中的非合作目标相对位姿测量问题,提出一种目标可测部位点云的智能配准方法。首先,通过Straight Through滤波算法对半物理仿真平台采集得到的点云进行目标提取,以消除背景数据等杂乱信息;其次,改进PointNetLK神经网络点云配准算法,将提取后的点云数据作为输入,从而获得初步配准结果,解决非合作目标先验信息缺失导致的无法配准问题;最后,建立基于位姿图的优化模型,以降低配准误差,提高配准精度。实验结果表明,与传统迭代最近点(ICP)算法相比,配准综合误差从6.3598降低到1.7291,精度提高约 72.81% 单次耗时从33.16 s降低到4.2 s,效率提升约87.33%,与当前SM ICP等其他算法相比,也具有一定的优势。     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.     

An extended chirp scaling algorithm for spaceborne sliding spotlight synthetic aperture radar imaging

A system impulse response with low sidelobes is critical in synthetic aperture radar（SAR） images because sidelobes contribute to noise and interfere with nearby scatterers. However,the conventional tricks of sidelobe suppression are unable to be exactly applied to the case of spaceborne sliding spotlight SAR due to great azimuth shifts in both time and frequency domains. In this paper, an extended chirp scaling algorithm is presented for spaceborne sliding spotlight SAR data imaging. The proposed algorithm firstly uses the spectral analysis（SPECAN） technique to avoid the azimuth spectrum folding effect and then employs the chirp scaling（CS） algorithm to achieve data focusing, i.e., the so-called two-step approach. To suppress the sidelobe level, an efficient strategy for the azimuth spectral weighting which only involves matrix multiplications and short fast Fourier transformations（FFTs） is proposed, which is a post-process executed on the focused SAR image and particularly simple to be implemented. The SAR image processed by the proposed extended CS algorithm is very precise and perfectly phase-preserving. In the end, computer simulation results verify the analysis and confirm the validity of the proposed algorithm.     

Grinding performance evaluation of porous composite-bonded CBN wheels for Inconel 718

For high-efficiency grinding of difficult-to-cut materials such as titanium and nickel alloys, a high porosity is expected and also a sufficient mechanical strength to satisfy the function.However, the porosity increase is a disadvantage to the mechanical strength. As a promising pore forming agent, alumina bubbles are firstly induced into the abrasive layer to fabricate porous cubic boron nitride（CBN） wheels. When the wheel porosity reaches 45%, the bending strength is still high up to 50 MPa with modified orderly pore distribution. A porous CBN wheel was fabricated with a total porosity around 30%. The grinding performance of the porous composite-bonded CBN wheel was evaluated in terms of specific force, specific grinding energy, and grinding temperature, which were better than those of the vitrified one under the same grinding conditions. Compared to the vitrified CBN wheel, clear straight cutting grooves and less chip adhesion are observed on the ground surface and there is also no extensive loading on the wheel surface after grinding.     

基于GA-ELM的飞行载荷参数识别

针对用复杂飞行数据识别飞行载荷时的精度低、速度慢等问题,提出一种结合遗传算法(GA)和极限学习机(ELM)的GA-ELM模型。该模型使用ELM神经网络作为计算核心,用遗传算法产生ELM网络输入层到隐含层的权值矩阵和隐含层偏移量;用GA-ELM模型对飞行数据进行识别,并与BP神经网络和原始ELM神经网络的识别结果进行对比。结果表明:GA-ELM模型是一种有效且高精度的飞行载荷参数识别方法。     

浅析运输类飞机经济舱头排旅客座椅头部损伤判据的影响因素

介绍了头部损伤判据HIC的概念、适航要求及影响运输类飞机客舱头排旅客座椅HIC的相关参数;叙述了旅客座椅头排距、头部撞击壁板的刚度、安全带织带伸长率、乘坐摩擦系数等参数的影响和重要程度;对如何设计符合HIC要求的经济舱头排旅客座椅和壁板提出了建议。     

民用飞机综合模块化航电系统分区和资源分配的研究

对比航电传统系统架构,分析了综合模块化航电系统(Integrated Modular Avionics,简称IMA)架构的优点;针对IMA系统功能的高集成度带来的资源分配难度和安全性要求,提出了IMA系统分区和资源分配策略,并给出了具体方法,对民用飞机IMA系统设计具有一定的指导意义和实用价值。     

基于A320飞机横航向控制律架构的LQ设计

空客A320飞机横航向控制律采用特征结构配置方法计算闭环增益,实现了期望特征值的配置及横航向关键参数之间的解耦。基于A320横航向控制律架构,提出使用线性二次型(Linear Quadratic,简称LQ)方法,对闭环增益进行重新设计。首先根据期望的特征值引入关键参数的频域整形环节,而后通过调整二次型性能指标加权矩阵实现期望特征值的逼近及关键参数之间的解耦,并求得闭环反馈增益,最后在特征结构、稳定裕度和横航向解耦效果方面与空客设计结果作了对比分析。仿真结果表明,LQ设计可获得与特征结构配置设计相近的结果,即得到接近期望值的特征值,并实现关键变量的解耦,同时保证了闭环稳定裕度。     

微小推力下小卫星的轨道递推与推力标定

针对采用微小推力进行轨道机动的小卫星,考虑复杂摄动力的基础设计了一种高精度轨道外推和推力在轨标定算法.首先,建立了考虑地球复杂摄动力和微小推力的小卫星轨道动力学模型;然后基于动力学模型,利用变步长龙格库塔算法,设计了对微小推力小卫星进行高精度轨道外推的方法.随后通过无迹卡尔曼滤波器(UKF),设计在轨标定算法,对存在误...