基带滤波调制技术深空测控应用性能分析

深空测控的下行链路包括测控和数传2种典型的传输信道,其中数传信道一般具有发射功率大和数据传输速率高的特点,其带外辐射容易对测控信道产生干扰。以典型月球探测器为例,分析比较了数传信道采用常规BPSK（二进制相移键控）调制和基带滤波BPSK调制时,对测控信道的带外辐射影响,并对基带滤波BPSK调制的误码率性能进行了仿真分析,相应结论对未来深空探测任务测控系统总体设计具有十分重要的参考意义。     

小波分析在外测数据滤波中的应用研究

为提高弹道估计精度,提出了一种基于小波分析的滤波方法,滤除外测数据中AR（自回归）模型的随机误差。分析讨论了滤波过程中的几个关键问题,提出了利用基于偏自相关系数截尾检验的方法来确定分解层数,然后采用GCV（广义交叉确认）准则来确定均方差意义下最优阈值的方法。本文提出的外测数据滤波方法计算简单,不需要估计噪声的方差。仿真结果表明,该方法能有效滤除外测数据中的AR噪声。     

某型导弹雷达通用磁控管自动老炼仪的设计

介绍了某型导弹雷达导引头通用磁控管自动老炼仪的组成和工作原理,重点介绍了磁控管电流检测过程中为消除噪声对检测结果的影响而采取的综合措施,实践表明,采取该措施后可以有效克服噪声对磁控管电流检测结果的影响。     

混合线性/非线性状态空间模型的边缘Rao-Blackwellized粒子滤波法(英文)

本文提出了边缘 Rao-Blackwellized 粒子滤波器(marginal Rao-Blackwellized particle filter, MRBPF)算法,算法融合了 Rao-Blackwellized 粒子滤波器(Rao-Blackwellized particle filter , RBPF)算法和边缘粒子滤波器(marginal particle filter, MPF)算法。算法中状态被分为线形和非线性两部分,分别用 MPF 和卡尔曼滤波器(Kalman Filter)进行估计。地形辅助导航(terrain aided navigation, TAN)的仿真结果表明,与 RBPF 相比,提出算法的非线性状态估计的误差均方根(root mean square error, RMSE)和误差方差分别降低了约 29%和 96%,独立粒子数提高了约80%,获得了更好的收敛结果。分析表明,现有RBPF是MRBPF的一个特例。     

MPDAF方法和DF方法在抗距离拖引干扰中的应用

利用Monte Carlo方法,对修正的概率数据互联滤波(MPDAF)和分解融合方法(DF)在有虚警测量的环境下抗距离拖引干扰的性能进行了分析和比较.研究表明,在虚警测量较少的情况下,采用MPDAF算法和DF算法可以有效地抗距离后拖欺骗干扰,实现对目标的跟踪,但是在虚警测量较多的情况下,采用DF方法可以较好地对抗距离后拖欺骗干扰,实现对目标的跟踪,而MPDAF方法则基本失去作用.     

推进剂利用系统连续液位测量滤波算法研究

针对我国低温推进剂利用系统中连续液位测量中的错节问题,提出了一种基于流量的液位测量滤波算法。介绍了该算法的基本原理,对算法进行了数学建模与数字仿真。通过对仿真结果的分析证实了该算法可以有效解决火箭飞行过程中由于液位晃动造成的测量误差问题,具有一定的工程应用价值。     

广义Hilbert变换及其滤波器的设计

广义Hilbert变换将传统的Hilbert变换由整数阶向分数阶的推广,其应用领域也得到了扩展。文章主要研究设计广义Hilbert滤波器时,窗函数的选择问题以及对滤波器的误差进行分析,并给出了分析结果。     

A Partially Orthogonal EnKF approach to atmospheric density estimation using orbital debris

A key requirement for accurate trajectory prediction and space situational awareness is knowledge of how non-conservative forces affect space object motion. These forces vary temporally and spatially, and are driven by the underlying behavior of space weather particularly in Low Earth Orbit (LEO). Existing trajectory prediction algorithms adjust space weather models based on calibration satellite observations. However, lack of sufficient data and mismodeling of non-conservative forces cause inaccuracies in space object motion prediction, especially for uncontrolled debris objects. The uncontrolled nature of debris objects makes them particularly sensitive to the variations in space weather. Our research takes advantage of this behavior by utilizing observations of debris objects to infer the space environment parameters influencing their motion.The hypothesis of this research is that it is possible to utilize debris objects as passive, indirect sensors of the space environment. We focus on estimating atmospheric density and its spatial variability to allow for more precise prediction of LEO object motion. The estimated density is parameterized as a grid of values, distributed by latitude and local sidereal time over a spherical shell encompassing Earth at a fixed altitude of 400 km. The position and velocity of each debris object are also estimated. A Partially Orthogonal Ensemble Kalman Filter (POEnKF) is used for assimilation of space object measurements to estimate density.For performance comparison, the scenario characteristics (number of objects, measurement cadence, etc.) are based on a sensor tasking campaign executed for the High Accuracy Satellite Drag Model project. The POEnKF analysis details spatial comparisons between the true and estimated density fields, and quantifies the improved accuracy in debris object motion predictions due to more accurate drag force models from density estimates. It is shown that there is an advantage to utilizing multiple debris objects instead of just one object. Although the work presented here explores the POEnKF performance when using information from only 16 debris objects, the research vision is to utilize information from all routinely observed debris objects. Overall, the filter demonstrates the ability to estimate density to within a threshold of accuracy dependent on measurement/sensor error. In the case of a geomagnetic storm, the filter is able to track the storm and provide more accurate density estimates than would be achieved using a simple exponential atmospheric density model or MSIS Atmospheric Model (when calm conditions are assumed).     

基于LTCC的基片集成波导滤波器仿真设计

文章详细论述了基于LTCC工艺的基片集成波导（SIW,Substrate Integrated Waveguide）滤波器的设计方法和步骤。并在此基础上,设计出了2个Ku频段具有椭圆函数响应的SIW滤波器。应用上下2个SIW谐振腔的耦合开孔位置不同,分别引入了正负耦合形式,从而能够实现具有椭圆函数响应的滤波器。验证了上下两腔之间为多层金属化耦合孔或单层金属形成的耦合窗,都可以达到引入耦合、进而实现特定的滤波响应的目的。这种基于LTCC的SIW滤波器具有尺寸小、设计灵活、电性能优良的优点。     

基于多敏感器的卫星姿态确定算法

针对多敏感器组成的卫星姿态确定系统,采用四元数方法建立卫星姿态估计器模型,应用扩展卡尔曼滤波EKF算法进行信息融合,通过仿真算例验证此算法具有较高的精度。与此同时,在EKF算法基础上提出一种定常增益的卡尔曼滤波CGKF算法。通过数学仿真对EKF算法和CGKF算法进行比较分析,证明在一定条件下CGKF算法具有与EKF算法相当的滤波精度,并且简化后的CGKF算法在获得与EKF算法相近滤波效果的同时,还能够节约星上资源,减少计算量。