Effects on noise properties of GPS time series caused by higher-order ionospheric corrections

Higher-order ionospheric (HOI) effects are one of the principal technique-specific error sources in precise global positioning system (GPS) analysis. These effects also influence the non-linear characteristics of GPS coordinate time series. In this paper, we investigate these effects on coordinate time series in terms of seasonal variations and noise amplitudes. Both power spectral techniques and maximum likelihood estimators (MLE) are used to evaluate these effects quantitatively and qualitatively. Our results show an overall improvement for the analysis of global sites if HOI effects are considered. We note that the noise spectral index that is used for the determination of the optimal noise models in our analysis ranged between −1 and 0 both with and without HOI corrections, implying that the coloured noise cannot be removed by these corrections. However, the corrections were found to have improved noise properties for global sites. After the corrections were applied, the noise amplitudes at most sites decreased, among which the white noise amplitudes decreased remarkably. The white noise amplitudes of up to 81.8% of the selected sites decreased in the up component, and the flicker noise of 67.5% of the sites decreased in the north component. Stacked periodogram results show that, no matter whether the HOI effects are considered or not, a common fundamental period of 1.04 cycles per year (cpy), together with the expected annual and semi-annual signals, can explain all peaks of the north and up components well. For the east component, however, reasonable results can be obtained only based on HOI corrections. HOI corrections are useful for better detecting the periodic signals in GPS coordinate time series. Moreover, the corrections contributed partly to the seasonal variations of the selected sites, especially for the up component. Statistically, HOI corrections reduced more than 50% and more than 65% of the annual and semi-annual amplitudes respectively at the selected sites.     

用纽曼随机有限元分析复合材料回旋结构的可靠性

将纽曼级数展开应用于蒙特卡罗随机有限元，对每一次随机抽样，只需一次形成刚度矩阵，进行前代、回代及矩阵的乘法和加法（减法）运算，可使随机有限元计算时间大为缩短。然后采用蒙特卡罗直接比较法，计算一复合材料回旋结构的可靠性。     

基于时间序列的空中交通流量灰预测模型算法

利用灰色预测理论,给出了一种空域内空中交通流量进行预测分析的算法——空中交通流量灰预测法,并利用Matlab软件作为计算工具,对管制区域、机场等空域流量的预测进行了仿真,计算结果以图表和图形的形式给出直观说明,验证了该算法的有效性。     

非线性时间序列在卫星通信网络数据预测中的研究

卫星通信网络数据预测在卫星通信系统中起着重要的作用,是系统建模的主要研究内容之一。由于卫星通信网络数据的非平稳性和不可预知因素的影响,决定了应采用非线性时间序列建模方法来分析、预测。在分析通信网络数据的基础上,建立卫星通信网络数据的ARIMA模型,在确定预测模型的阶和进行参数估计后,给出不同预测步数条件下的通信网络数据流量的预测,并进行了仿真对比实验。仿真结果表明,该模型在预测步数较小的情况下,预测误差在4％左右,具有良好的预测精度,为卫星通信网络数据流量的预测、异常检测和网络负载预测的应用奠定了坚实的基础。     

基于模型预测控制的非线性飞行控制系统研究

应用泰勒级数展开方法设计了飞行器六自由度非线性模型预测控制器.通过对控制向量和输出向量的有限泰勒级数展开,确定闭环形式的控制输入.仿真结果表明:该方法能够适应控制项不完全或缺少控制项的病态情况,适当选择控制器的泰勒展开阶数可得到最优的控制结果,所设计的模型预测控制器具有很好的跟踪效果.     

Combining frequency and time domain models to forecast space weather

Space weather series incorporate several distinct components, cycles at multiple frequencies, irregular trends, and nonlinear variability. The cycles are stochastic, i.e., the amplitude varies over time. Similarly, the trend is stochastic: the slope and direction of trending change repeatedly. This study sets out a combined model using both frequency and time domain methods, in two stages. In the first stage, a frequency domain algorithm is estimated and forecasted. In the second stage, the forecast is used as an input in a neural network. The combined model also includes a term enabling the model to react inversely to large deviations between the actual values and forecast. The models are evaluated using two data sets, the hemispheric power data obtained from the Polar Orbiting Environment satellites, and the Aa geomagnetic index. All the series are at a daily resolution. Forecasting experiments are run over horizons of 1–7 days. The models are estimated using a moving window or adaptive approach. The combined model consistently achieves the most accurate results. Among single equation methods, the frequency domain model is more accurate for the geomagnetic index because it is able to capture the underlying cycles more effectively. In the hemispheric power series, the cycles are less pronounced, so that time domain methods are more accurate, except at very short horizons. Nevertheless, in both data sets, the combined model works well because the frequency domain algorithm captures cyclical behavior, while the neural net is better able to capture short-term dependence and trending.     

定位环系列产品的工艺及模具设计

按定位环系列产品外径大小,设计了三种定位环系列产品压制模具结构,不仅结构合理,制造成本低,而且做到了一模多用。     

航材消耗的时间序列分析

本文采用时间序列分析法对某种航材故障率进行预测,用移动平均法消除时间序列的长期趋势和周期变动,然后按月平均法求出季节指数,并对季节指数进行调整,然后根据拟合的趋势方程对此种航材的故障率进行预测.其结果能为航材的可靠性保障提供理论依据.     

基于多小波的Volterra级数非定常气动力建模方法

非定常气动力建模除了要准确描述气动力的非定常特性,还要反映其非线性特性。Volterra级数因为对系统非线性具有很强的描述能力正日益受到重视。一阶Volterra核只能表达线性特性,要建立反映非线性特性的非定常气动力模型,需要引入二阶核甚至更高阶核的影响。高阶Volterra核辨识的主要困难在于待辨识参数的数量随着核阶次的增加而呈指数增加导致计算难度急剧加大,即出现所谓维数灾难问题。以一种分段二次多小波为基函数将Volterra核展开,求解一个高维病态方程组来计算展开系数,利用小波的多分辨分析在时间和频率两个维度的分解特性将方程降维,最终将问题转化为求解一个低维方程组得到稳定解。通过NACA0012翼型在马赫数0.8下作沉浮运动时,升力系数、阻力系数和俯仰力矩系数的二阶核和三阶核的辨识构建非定常气动力模型,然后由此计算不同减缩频率下的气动力并与CFD结果进行比较,验证了Volterra级数对非线性非定常气动力的描述能力和多小波处理方法的有效性。     

容错航空蓄电池电源及其均衡管理

航空蓄电池在实际使用中会出现电池性能衰退与退化速率不一致,电池组容量与使用寿命降低,甚至可能出现严重的安全事故。针对电池单体不一致性和故障隔离问题,提出一种蓄电池电源系统容错体系结构和分级容错控制策略,提出基于电池单体动态重构的主动均衡管理新方法,设计基于"冒泡沉底（BS）择优上岗"的电池单体实时动态重构策略。开发容错航空镍镉电池电源原理样机,给出系统实验性能分析,实验结果表明所提出的系统技术方案可行有效,能够快速隔离故障失效电池单体,明显改善电池不一致性,提高了电池组容量利用率和剩余使用寿命。