CHAMP卫星加速度计标校参数序列的谱分析

给出了利用能量守恒方法求定CHAMP卫星加速度计标校参数的基本原理和算法．对2001年8月-2003年12月共约两年半的CHAMP卫星加速度计数据进行了标校,导出了尺度、偏差和偏差漂移参数的时间序列,分别采用最小二乘谱分析和抗差谱分析对这些时间序列进行了分析．结果表明,尺度、偏差和偏差漂移参数都有明显的周期变化,但各主周期项略有差异．尺度的主周期是441d、220d、126d和13．4d的周期变化,其中220d和441d的周期变化的贡献相对较大．偏差中有明显的220d、126d和13．4d周期变化,各周期变化的贡献几乎相当;偏差漂移的主周期是220d、13．4d和9d的周期变化,其中13．4d的周期变化最为显著,几乎比其他周期项变化高出一个数量级．另外尺度和偏差参数还有明显的长期变化,而偏差漂移参数的长期变化不明显．     

Shaping low-thrust trajectories with thrust-handling feature

Shape-based methods are becoming popular in low-thrust trajectory optimization due to their fast computation speeds. In existing shape-based methods constraints are treated at the acceleration level but not at the thrust level. These two constraint types are not equivalent since spacecraft mass decreases over time as fuel is expended. This paper develops a shape-based method based on a Fourier series approximation that is capable of representing trajectories defined in spherical coordinates and that enforces thrust constraints. An objective function can be incorporated to minimize overall mission cost, i.e., achieve minimum $\mathrm{\Delta }V$. A representative mission from Earth to Mars is studied. The proposed Fourier series technique is demonstrated capable of generating feasible and near-optimal trajectories. These attributes can facilitate future low-thrust mission designs where different trajectory alternatives must be rapidly constructed and evaluated.     

管道喷涂机器人:结构与位姿调整

针对工业上大型管道内壁喷涂的需求,设计了用于喷涂管道的机器人,该机器人采用串并联结构形式,由支撑平台和串联机械手组成.支撑平台采用几何的方法建立数学模型,串联机械手采用D-H参数法建模;并在数学模型基础上给出了逆解方程.位姿调整时,对机器人进行初定位,利用激光跟踪仪测量出管道和机器人的位姿,求解出调整量,对机器人进行位姿调整,使喷枪旋转轴和管道轴线重合.管道喷涂机器人在工厂进行试验测试,测试结果表明该装备可以很好地实现管道的喷涂,提高喷涂质量和效率.      

多分量雷达辐射源信号的检测与分离

针对多分量雷达辐射源信号难以有效检测与分离的问题,提出了时频变换结合时变滤波的方法。通过图像分割中的区域生长法在时频平面对各分量进行检测,对其中的线性调频分量采用分数阶傅里叶变换进行优先分离,再对时频或频域没有耦合的分量进行滤波,最后通过支持向量机划分分类线,用于时频耦合分量的分离。仿真实验结果表明,该方法可以对多分量信号进行有效的检测与分离。     

The Big Pine Creek watershed and climate change: A trend analysis of Landsat surface reflectance and PRISM datasets over the last 3 decades

Recent variations in normal meteorological conditions indicate the earth’s climate is changing in ways that may impact delicate ecological balances in sensitive regions. Identifying how those changes are affecting the biosphere is essential if we are going to be able to adapt to those changes and to potentially mitigate their harmful consequences. This paper presents a time series study of an alpine ecosystem in the Big Pine Creek watershed in California’s Eastern Sierra Nevada Mountain’s. Raw Landsat data covering the years 1984 through 2011 is converted to observed surface reflectance and analyzed for trends that would indicate a change in the ecosystem. We found that over the time period of the study, observed surface reflectance shows a general decline across the spectrum while our analysis of environmental data demonstrates statistically significant increases in temperatures. While declining reflectance in the visible and short wave bands are indicators of increased surface cover, the fact that the IR band also shows declines is consistent with a decline in tree density. This study provides a useful insight into the ecological response of the Big Pine Creek watershed to recent climate change. These findings suggest that alpine ecosystems are particularly sensitive to increasing temperatures. If these results are replicated in other alpine watersheds it will demonstrate that the biosphere is already showing the effects of a warmer environment.     

高超声速流场对自由流扰动波响应及边界层扰动波演化

采用高阶精度有限差分方法直接数值模拟了来流慢声波作用下的钝锥高超声速绕流瞬态流场,分析了自由流扰动波与流场的相互干扰,并应用Fourier频谱分析了边界层扰动波时域和空域演化.结果显示:弓形激波出现连续的“∽”变形,初始扰动被显著放大,边界层内外扰动模态存在明显差别.从空域(沿流线)演化来看,在球头附近低频扰动为主导模态,出了球头区,总扰动模态中的低频和高频成分比例迅速转变,高频模态成分显著地增大.至流场下游,大部分低频分量衰减,或者不再增长,边界层内仅存在特殊频率的不稳定波快速增长.从时域演化来看,比起其他模态,主导模态的发展对上游激励的依赖更大.无论时域还是空域演化,都存在模态竞争现象.      

基于自相关及相位差法的高精度频率估计算法

为提高高斯白噪声背景中正弦信号的频率估计精度,对基于自相关运算的频率估计算法的相关长度m进行了推导,得到了m的优化值与信噪比的关系式.当信噪比较高时,m的最优值为N/3(N为信号采样点数);信噪比较低时,m的最优值为N/2.通过对自相关法及分段FFT(Fast Fourier Transforms)相位差法特性的分析,提出了一种性能更优的频率估计综合算法. Monte Carlo仿真实验表明:新算法吸收了两种算法的优点,克服了其不足,在更大的信噪比范围内具有较高的频率估计精度,且计算量也较小.      

利用神经元拓展正则极端学习机预测时间序列

为实现对于时间序列预测数据的准确预测,提出一种神经元拓展正则极端学习机(NERELM,Neuron-Expanding Regularized Extreme Learning Machine),并研究了其在时间序列预测中的应用.NERELM根据结构风险最小化原理权衡经验风险与结构风险,以逐次拓展隐层神经元的方式自动确定最佳的网络结构,以避免传统神经网络训练过程中需人为确定网络结构的弊端.应用于时间序列的仿真结果表明:NERELM可有效实现对于RELM最佳网络结构的自动确定,具有预测精度高与计算速度快的优点.      

串并混联空间机械臂全自由度轨迹规划算法

目前,国内外对串并混联空间机械臂的研究较少;而且大多在舍弃冗余自由度的情况下进行轨迹规划.针对7自由度串并混联空间机械臂,提出了一种基于解析解、并且充分放开全部7个自由度的机械臂轨迹规划算法.该规划算法既扩大了串并混联空间机械臂的规划可达空间,又保证了规划的实时性,同时还保留了结构承载能力强的优点,可以充分发挥其作为空间抓捕用臂的优势.通过仿真和地面空间操作抓捕试验,验证了算法的有效性.