基于主成分分析法的地磁导航适配区域选择

地磁场信息量越大的区域,导航性能越好。借用数理统计和信息论中的概念,设定了多个评价地磁场信息特点的指标,并通过主成分分析法,排除了相关指标,得到综合指标值,客观、定量地分析了适配区的导航性能,从而选择最佳导航适配区。仿真表明,本文方法是一种选择地磁导航工作区的有效方法,选择的适配区导航误差小。     

基于半实物仿真的地磁导航等值线匹配算法评估

影响地磁导航匹配算法性能的因素很多,而现阶段算法的评估完全依靠计算机仿真,其可信性有待进一步验证.以等值线(ICCP)算法为研究对象,首先从理论上分析影响算法性能的因素;然后搭建了地磁匹配导航半实物仿真系统,通过引入磁场仿真环境和磁传感器,提高了仿真的可信度;最后从测量噪声、匹配长度、匹配区域和惯导误差4个方面对ICCP 匹配算法的性能进行半实物仿真试验分析.仿真结果表明,通过半实物仿真试验可以对算法的抗干扰性、算法匹配长度的确定、匹配区域的选择以及惯导误差的影响做出有效评估,从而推动地磁匹配导航及匹配算法的工程化进程.      

Signatures of strong geomagnetic storms in the equatorial latitude

Ionosonde data from two equatorial stations in the African sector have been used to study the signatures of four strong geomagnetic storms on the height – electron density profiles of the equatorial ionosphere with the objective of investigating the effects and extent of the effects on the three layers of the equatorial ionosphere. The results showed that strong geomagnetic storms produced effects of varying degrees on the three layers of the ionosphere. Effect of strong geomagnetic storms on the lower layers of the equatorial ionosphere can be significant when compared with effect at the F2-layer. Fluctuations in the height of ionization within the E-layer were as much as 0% to +20.7% compared to −12.5% to +8.3% for the F2-layer. The 2007 version of the International Reference Ionosphere, IRI-07 storm-time model reproduced responses at the E-layer but overestimated the observed storm profiles for the F1- and F2-layers.     

航天器表面充电仿真计算和电位主动控制技术

文章运用等效电路理论推导出随时间变化充电问题的微分方程组,用FORTRAN语言开发了相应的计算机模拟程序,针对强地磁亚暴空间环境分析了地球同步轨道航天器在阴影区和光照区的充电水平。最后计算讨论了采用空心阴极等离子体接触器向航天器外发射电子束作为控制航天器充电水平手段的作用效果。     

一种利用剩磁标定的星光/磁测备份自主导航方法

针对空间机动平台GNSS导航系统易受干扰的缺陷,提出一种基于剩磁标定的磁测/星光备份的自主导航方案。当GNSS信号完好时,利用GNSS高精度测量信息和磁强计/星敏带剩磁干扰的联合测量信息不仅可实时估计出机动平台导航参数,同时准确标定出运行环境的剩余磁场强度;当GNSS信号受干扰中断时,在剩磁准确标定的基础上启用磁场/星光备份自主导航方案完成机动平台的导航参数实时估计。由仿真结果可知,当GNSS信号正常时该导航方案具备较高的剩磁标定精度,三轴标定误差为0.026nT,0.293nT,0.107nT;而当GNSS信号受干扰时,备份导航方案三轴位置估计误差为87.3m,172.5m,65.2m,三轴速度估计误差为0.78m/s,0.86m/s, 1.04m/s。 仿真结果表明该方案具备较强的可行性。     

Characteristics of field line resonance type geomagnetic pulsations and variations of plasmaspheric plasma density distribution

The period of field line resonance (FLR) type geomagnetic pulsations depends on the length of the field line and on the plasma density in the inner magnetosphere (plasmasphere), where field lines are closed. Here as FLR period, the period belonging to the maximum occurrence frequency of the occurrence frequency spectrum (equivalent resonance curve) of pulsations has been considered. The resonance system may be replaced by an equivalent resonant circuit. The plasma density would correspond to the ohmic load. The plasma in the plasmasphere originates from the ionosphere, thus FLR period, occurrence frequency are also affected by the maximum electron density in the ionosphere. The FLR period has shown an enhancement with increasing F region electron density, while the occurrence frequency indicated diminishing trend (possible damping effect). Thus, the increased plasma density may be the cause of the decreased occurrence of FLR type pulsations in the winter months of solar activity maximum years (winter anomaly).     

中低轨道地磁动态模拟器

文章介绍了1台能为中低轨道飞行器提供动态地磁场环境的模拟器,详细论述了模拟器的系统组成、线圈结构、电源和磁强计指标参数、控制方式和调试方法,介绍了根据WMM2000地磁场模型计算出轨道上各点的磁场值,利用数值分析的方法找出输出电流与产生磁场大小的对应表达式,从而根据计算出的磁场值和输出电流,用程控电源成功实现了中低轨道地磁场的动态模拟.     

Classification of solar wind structures and intense geomagnetic storm alarms with self-organizing maps

Intense geomagnetic storms (D_st < −100 nT) usually occur when a large interplanetary duskward-electric field (with Ey > 5 mV m⁻¹) lasts for more than 3 h. In this article, a self-organizing map (SOM) neural network is used to recognize different patterns in the temporal variation of hourly averaged Ey data and to predict intense storms. The input parameters of SOM are the hourly averaged Ey data over 3 h. The output layer of the SOM has a total of 400 neurons. The hourly Ey data are calculated from solar wind data, which are provided by NSSDC OMNIWeb and ACE spacecraft and contain information on 143 intense storms and a fair number of moderate storms, weak storms and quiet periods between September 3, 1966 and June 30, 2002. Our results show that SOM is able to classify solar wind structures and therefore to give timely intense storm alarms. In our SOM, 21 neurons out of 400 are identified to be closely associated with the intense storms and they successfully predict 134 intense storms out of the 143 ones selected. In particular, there are 14 neurons for which, if one or more of them are present, the occurrence probability of intense storms is about 90%. In addition, several of these 14 neurons can predict big magnetic storm (D_st  −180 nT). In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction.     

信鸽归巢导航的多信息融合

信鸽远距离导航能力一直以来都是生物导航领域研究的热点问题,其奥秘的逐步展露和揭示对仿生导航乃至仿生学的发展都有着十分重要的启发意义.论文归纳了基于环境的信鸽导航理论、基于信鸽感测能力的导航理论以及基于信鸽生理特征的导航理论的研究情况,介绍了近些年信鸽导航研究领域的新进展,展望了未来可能取得的突破方向.