基于改进ORB的月面着陆视觉导航的配准方法

月球探测器在下降着陆过程中探测到的月面图像灰度均匀,缺少纹理信息,而视觉辅助导航采用传统ORB算法对灰度均匀的月面图像检测出的特征点数量较少,容易出现团簇现象,因此提出一种基于改进ORB算法的图像特征提取与匹配算法.首先对月面图像构建金字塔尺度空间模型,设置自适应阈值并构建掩膜逐一分块检测特征点;然后采用非极大值抑制使...     

一种MEMS陀螺仪零偏建模与估计方法

针对MEMS陀螺仪零偏随时间变化的问题,提出了一种MEMS陀螺仪的零偏建模与估计方法.通过分析静态条件下陀螺仪零偏变化的影响因素,建立了陀螺仪零偏和温度之间的差分方程模型.采用系统辨识的方法,给出了系统模型的参数辨识流程.基于最小二乘法,实现了模型参数的辨识.设计了测试验证试验,基于陀螺仪的零偏样本特性建立了三阶差分方...     

基于背景扣除的弱吸收信息精确提取方法研究

随着半导体器件的快速发展，基于分子振-转吸收光谱的研究日益深入，以可调谐半导体激光吸收光谱（Tunable Diode Laser Absorption Spectroscopy， TDLAS）为代表的检测技术取得巨大进步，应用领域进一步扩大，已有超过一千类仪器应用于连续监测以及过程控制，每年出售的气体检测仪器占据了红外气体传感检测仪器总数的5%以上，已实现不同领域组分浓度、温度、压力等参数的高精度探测。本文针对仪器开发领域目标吸收信息被完全覆盖的复杂应用环境，利用调制技术将吸收信息转移到高频部分，经过分通道背景扣除和谐波信号归一化处理提取吸收信息。以测量含硫天然气中微量H₂S为例，天然气中CH₄含量超过90%，目标组分H₂S的吸收信息被完全覆盖，将吸收信息转移到1 kHz频率，得到的谐波信号峰值与H₂S浓度呈正相关，线性参数达到0.999 9，实现了弱吸收信号的有效提取，从方法上验证了提取弱吸收信号的有效性，进一步扩展了光学气体传感的应用领域，为仪器开发提供技术保障。     

基于混合神经网络的无线电信号自动调制识别

随着无线电信号数据海量增加，复杂电磁环境下面临着未知威胁和目标侦察识别复杂度高的问题，本文针对未知无线电信号的特征提取任务，设计了一种混合神经网络以提高目标无线电信号的识别能力。先通过胶囊神经网络对未知信号的空间信息进行提取，再进一步运用门控循环单元提取信号在时间上的特征信息。设计混合网络模型将信号的时间和空间特征相结合，提高对目标信号的分类精度。通过RML2016.04C调制信号数据集，验证了混合神经网络的识别性能。结果表明:当信噪比为6 dB时，混合网络模型对多种不同调制信号的分类精度大于95%。因此，本文所设计的混合神经网络能够有效对不同调制信号进行准确分类。     

基于点云深度学习的对称结构空间目标相对位姿测量

针对对称结构空间目标相对位姿解算过程中点云误匹配带来的误差问题，提出一种基于点云深度学习的对称结构空间目标相对位姿测量方法。首先设计空间目标点云特征提取网络及关键点回归网络，将位姿测量问题转换为空间目标点云关键点回归问题，通过两个并行的回归网络分别输出空间目标平移向量和具有固定标签的目标点云三维边界框角点；其次利用具有连续稳定标签的角点求解目标姿态，可有效解决目标的对称结构导致的点云误配准问题；最后通过仿真数据集的实验表明，该方法相比于传统的点云配准方法有更高的准确率，能够精确求解具有对称结构的空间目标相对位姿。     

一种航天测控设备技术状态快速自动建立方法

根据传统测控设备技术状态的建立方法,每个参数宏对应唯一一种技术状态,一个测控任务可能需要装订上百个甚至上千个参数宏,当航天器技术状态发生变化时,还需要人工操作更换参数宏,这为测控设备技术状态的建立、设备操作、应急处理和技术状态管理带来了很大困难.提出了一种测控设备技术状态快速自动建立方法,设计了支持多测控体制、多工作频...     

Performance improvement of licklider transmission protocol in complex deep space networks based on parameter optimization

A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol (LTP). Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol (BP). However, the research results are not applicable to communications in Complex Deep Space Networks (CDSNs) without the custody mechanism of BP that are more suitable for deep space communications with LTP. In this paper, we propose a model of file delivery time for LTP in CDSNs. Based on the model, we propose a Parameter Optimization Design Algorithm for LTP (LTP-PODA) of configuring reasonable parameters for LTP. The results show that the accuracy of the proposed model is at least 6.47% higher than that of the previously established models based on simple scenarios, and the proposed model is more suitable for CDSNs. Moreover, the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan. Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77% compared with configuring the other parameter configuration plans.     

Experimental and numerical investigation on opposing plasma synthetic jet for drag reduction

Experimental and numerical studies are carried out to validate the potential of opposing Plasma Synthetic Jet (PSJ) for drag reduction for a hemisphere. Firstly, flow field changes of opposing PSJ are analyzed by comparing the experimental schlieren images and simulation results in a supersonic free stream of Mach number 3. As PSJ is a kind of unsteady pulsed jet, the shock standoff distance increases initially and then decreases under the control of PSJ, which corresponds to the change of the strength of PSJ. Accordingly, the amount of drag reduction of the hemisphere increases initially and then decreases. It is found that there is a short period of “drag rise” during the formation of PSJ before the drag reduction, which is induced by the generation of normal shock waves and the area difference of the cavity wall of PSJ Actuator (PSJA). Secondly, the effects of five parameters, including exit diameter, discharge energy of PSJA, Mach number, static pressure of incoming flow and angle of attack, on drag reduction of opposing PSJ were studied in detail by using numerical method. It is found that the Maximum Pressure Ratio (MPR) has a significant impact on the average drag reduction for a configuration-determined PSJA. For the configuration selected in this study, the flow field of opposing PSJ shows typical Short Penetration Mode (SPM) in a control cycle of PSJ when the MPR is less than 0.89. However, the flow field shows typical Long Penetration Mode (LPM) at some time when the MPR is bigger than 0.89. Relatively better drag reduction is achieved in this case.     

Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images

Applications including change detection, disaster management, and urban planning require precise building information, and therefore automatic building extraction has become a significant research topic. With the improvements in sensor and satellite technologies, more data has become available, and with the increased computational power, deep learning methods have emerged as successful tools. In this study, U-Net and FPN architectures using four different backbones (ResNet-50, ResNeXt-50, SE-ResNext-50, and DenseNet-121), and an Attention Residual U-Net approach were used for building extraction from high-resolution aerial images. Two publicly available datasets, Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset were used to train and test the models. According to the results, Attention Residual U-Net model has the highest F1 score with 0.8154, IoU score with 0.7102, and test accuracy with 94.51% on the Inria dataset. On the Massachusetts dataset, FPN Dense-Net-121 model has the highest F1 score with 0.7565 and IoU score with 0.6188, and Attention Residual U-Net model has the highest test accuracy with 92.43%. It has been observed that, FPN with DenseNet backbone can be a better choice when working with small size datasets. On the other hand, Attention Residual U-Net approach achieved higher success when a sufficiently large dataset is provided.     

Adaptive sliding mode control of spacecraft attitude-orbit dynamics on SE(3)

In this paper, to solve the problem of parameters uncertainty in spacecraft tracking control, an adaptive controller based on sliding mode is proposed for the relative spacecraft attitude-orbit dynamics on the Lie group SE(3). The dynamic equations of relative attitude orbit error for two spacecraft are established in the framework of Lie group SE(3). Considering the uncertainty of spacecraft parameters, a formal decomposition of known and unknown parameters, the state variables and control variables is firstly made in the original system. An online estimator is designed to evaluate the unknown parameters. A sliding mode controller is developed to actuate the spacecraft to track the target spacecraft. Then a Lyapunov function of tracking error and parameters estimated error is designed to prove the stability of the closed-loop system. Finally, the simulation results and analysis are presented to verify the effectiveness and feasibility of the proposed method.