一种基于标准弹道法的自适应制导律设计

提出一种基于自适应补偿思想的再入飞行器纵向制导律改进方法.首先利用未知摄动函数描述纵向动力学方程,并基于此设计了对气动参数变化具有自适应能力的控制律,与原标准轨道控制律叠加形成全控制律,最终用于再入制导.仿真结果表明,所设计的控制律对于气动参数变化,大气密度变化具有较好补偿效果.     

一种新型恒温—温补晶体振荡器

提出了一种ＯＣＸＯ的恒温控制方法。该方法是利用乙醇的沸点制成换态式恒温箱。这种温度控制方法既可以用作中准确度的ＯＣＸＯ，又可以作为精密双层控温ＯＣＸＯ的外层恒温装置，还可以与简单的温补线路结合，构成高准确度的ＯＴＣＸＯ。     

低相位噪声10次倍频器研究

研制了低相位噪声10次倍频器。采用了5、2次倍频级联方法,使倍频器在100MHz/0dBm信号输入下,输出1GHz/10dBm信号,杂散小于<-55dB。介绍了倍频器的设计思路、调试方法和测试结果等。     

改善高频宽压控晶振频率温度稳定性方法

高频宽压控晶体振荡器广泛应用于各种接收机和应答机中,工作温度范围通常为-40℃～+85℃。对于该类晶振,极易出现频率温度稳定性相对于其内部石英谐振器明显恶化的现象。对此,从理论上分析了引起恶化的原因,并提出通过合理控制振荡电路的压控范围和石英谐振器的激励功率,可以改善高频宽压控晶振的温频特性。最后,研制并测试了4只102.3MHz压控晶振,结果优于指标要求,充分验证了方法的有效性。     

一种新型陀螺的力矩器非圆性误差补偿方法

为提高磁悬浮控制敏感陀螺（MSCSG）对陀螺载体姿态的敏感精度,基于其洛伦兹力磁轴承（LFMB）的设计结构,提出了一种力矩器非圆性误差补偿方法。首先,针对一种新型双球形包络面转子MSCSG,介绍了MSCSG的结构特点与陀螺载体姿态角速度敏感原理,并分别建立了MSCSG力矩器半径误差模型、转子偏转干扰力矩模型与陀螺载体姿态角速度敏感误差模型。其次,通过实验测量了力矩器的圆度,通过MATLAB进行数据拟合得到了力矩器的非圆特性,采用勒让德多项式级数对力矩器非圆性进行了描述,并有效补偿了因力矩器非圆性误差导致的姿态角速度敏感误差。最后,对误差补偿效果进行了仿真验证,结果表明该补偿方法使陀螺载体姿态角速度敏感误差降低了83.5%。此外,本文方法还可以解决LFMB陀螺的相关共性问题。      

紧缩场测试扫描架平面度控制系统研制

扫描架是紧缩场现场校准系统的关键测试设备。扫描架扫描平面度是影响校准准确度的关键指标。本文针对自研的扫描区域为φ6m的大型极化扫描架,设计了一套扫描架平面度实时补偿系统。该实时补偿系统可以实时检测扫描架扫描平面相对于基准平面的位置偏差,并通过伺服补偿机构完成补偿。测试结果表明该实时补偿系统可以准确、快速完成平面度的补偿,与半实时补偿方式相比具有更高的效率及精度。目前,该系统已成功应用于紧缩场现场校准。     

柔性机械臂辅助空间站舱段对接阻抗控制

空间机械臂辅助舱段对接过程中存在测量与控制误差,易导致对接机构间存在较大接触力,传统FMA (Force MomentAccommodation)控制方法在测量接触力时无法消除大负载惯性力对测量的影响,且测量仪器的引入会进一步降低空间柔性机械臂的刚度。为此,文章提出了柔性机械臂辅助大负载空间舱段对接的阻抗控制方法,采用拉格朗日法推导了空间机械臂的关节输入力矩方程作为前馈输入,建立了含动力学前馈的空间机械臂阻抗控制程序,并以在商业软件ADAMS中建立的空间柔性机械臂与对接舱段组成的系统动力学模型作为控制对象,对系统进行ADAMS灢Matlab联合仿真。仿真结果表明,按照此控制方法,系统可克服外力干扰使目标解析点按照期望的方式运动;同时,通过测量机械臂关节运动参数即可实现对外力的准确感知,而不需额外添加力传感器,既消除了大负载惯性力对测量的影响,也不会导致柔性机械臂刚度的降低。     

一种指令接收机载波扫频适应性测试方法

介绍了一种指令接收机载波扫频适应性测试方法。运用Labview软件,控制合成信号源输出频率的变化,模拟地面站扫频信号模式,测试指令接收机载波扫频适应性。经过实践,证明该方法能够满足测试要求。     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.