GPS不完整星座定位方法研究

为了提高钟差预测模型辅助接收机定位解算的效果,从灰色系统的角度对接收机钟差序列进行探讨,提出了基于灰色理论的钟差预测模型。文中首先给出了新模型的基本思想和具体实现步骤,然后将基于该模型的钟差预测值引入到GPS接收机中,辅助接收机在不完整星座条件下实现定位解算。基于GPS实测数据的验证分析表明,新模型不仅对钟差序列具有很好的预测效果;而且提高了接收机定位的连续性和可靠性,在仅有3颗卫星的条件下可以实现三维定位。同时,新方法无需增加额外设备,辅助方式简单方便,经济灵活。     

用灰色模型预测卫星太阳电池阵输出功率

研究了GM（1，1）模型的建模方法，建立了太阳电池阵输出功率的GM（1，1）预测模型，针对电池出输出功率具有随季节波动的变化趋势，提出了采用实时在线的方法，建立动态新息GM（1，1）预测模型，经实例预测验证，动态新息GM（1，1）模型可明显地提高预测精度，且能对电池阵输出功率的波动趋势正确预测。     

基于最小二乘支持向量机的小样本建模方法研究

针对捷联惯组历次测试数据小样本建模问题,提出了通过二次修正插值方法解决测试数据的非等间隔性和样本容量小的问题。并通过相空间重构的思想将一维时间序列多维化。最后通过最小二乘支持向量机建立预测模型。实例分析表明,建立在二次修正插值基础之上的最小二乘支持向量机时间序列模型具有较高的预测精度,能够很好地满足对惯组测试数据分析的要求。     

动态测量系统的抗扰性预报算法

为了克服采样数据可能包含的野值对状态预报的不利影响，本文在动态测量系统的有界影响滤波技术的基础上提出了状态预报的两组抗扰性算法，并进行了仿真计算。     

空间碎片自然衰减和太阳活动

分析研究了空间碎片数随太阳辐射流量Ｆ１０．７的变化;给出预报Ｆ１０．７长期变化的计算方法和预测空间碎片数的数学模型。结果显示：①强太阳活动造成空间碎片年增长率下降;②空间碎片数与太阳活动１１年变化密切相关,相关数为０．９;③空间碎片增长率约为发射率的两倍;④若发射率保持不变,则到２０２０年,大于１０ｃｍ的碎片数将达到１４５００;⑤若小碎片的增长为大碎片增长的两倍,则到２０２０年,大于１ｃｍ的碎片数可达１２５０００。     

Shareable Instruments and Observatories for the Joint Research Center on Space Weather of the Siberian Branch of Russian Academy of Sciences and Chinese Academy of Sciences

Presented are the ideas and proposals in regards to the pooling of by RAS, SB, ISTP, and CSSAR,CAS toward coordinated usage of existing ground-based and orbiting helio-geophysical observatories, single large installations as well as creating, forecasting services and new observing facilities, in the interests of achieving a profitable activity of the China-Russia Joint Research Center on Space Weather (JRCSW).      

Forecasting space weather: Can new econometric methods improve accuracy?

Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A_p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A_p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.     

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.     

支持向量机时间序列预测模型的参数影响分析与自适应优化

建立在统计学习理论和结构风险最小原则上的支持向量机在理论上保证了模型的最大泛化能力,因此与建立在经验风险最小原则上的神经网络模型相比,理论上更为完善.本文运用支持向量机建立时间序列预测模型,研究影响模型预测精度的相关参数,在分析参数对时间序列预测精度的影响基础上,提出用遗传算法建立支持向量机预测模型的参数自适应优化算法.最后,用太阳黑子数据和航空发动机油样光谱数据进行了预测分析.算例表明了本文算法的正确性.