基于连续小波变换的飞行器结构模态参数辨识

给出了一种基于连续小波变换的多输入多输出MIMO(Multiple-Input Multiple-Output)飞行器结构模态参数的辨识方法.对结构离散运动方程进行连续小波变换建立了小波域内的系统AR(Auto Regression)模型,AR模型的系数矩阵决定着系统的动力学特性,可以通过最小二乘法求得.模态参数可以通过求解由AR系数矩阵构成的特征矩阵的特征值来获得.与已有基于小波变换的模态参数辨识方法相比,该方法应用了连续小波的时移共变性和小波变换的滤波能力来确保辨识的效率.在辨识过程中,采用优化算法提高了辨识的精度和稳定性.算例仿真结果表明该方法具有较高的计算精度和稳定性,能用于飞行器结构模态参数的辨识.      

MEMS陀螺仪随机误差滤波

针对微机电系统(MEMS,Micro Electromechanical System)陀螺仪的随机漂移,基于小波多尺度分析,利用bior1.5小波对陀螺仪的随机漂移进行深度为4的分解,重建各尺度信号,采用时间序列方法对陀螺仪各尺度随机漂移进行建模,与传统时间序列方法建模相比,降低了模型的预测误差.并构建了模糊自适应Kalman滤波,利用模糊控制方法基于残差均值与方差差值对噪声方差阵进行实时调整,提高对重建后的各尺度信号随机噪声滤波效果.通过一系列对比实验证明,基于多尺度分析的模糊自适应Kalman滤波对于消除MEMS陀螺仪随机漂移误差作用明显.通过Allan方差分析,滤波后的数据各随机误差项均得到有效减小.     

On the clustering of foF2 time series corresponding to disturbed ionospheric periods

This paper presents an analysis of a set of time series that represent foF2 disturbances during storm conditions, using clustering tools. The time series under study have been drawn from ionospheric observations obtained from eight European middle latitude ionosondes during a significant number of storm-time intervals and they are divided into eight groups according to the latitude (middle to low and middle to high) and the local time of the observation point at storm onset (afternoon, evening, morning, prenoon). The time series in each group have been analyzed using clustering-based methods. Specifically, each time series has been represented using two different ways of representation: the first is the raw representation while the second is through the parameters of the autoregressive (AR) model that best represents it. For each representation a hierarchy of clusterings is produced via the complete link algorithm. The two produced hierarchies are combined to a single one and the final clustering results are extracted from the produced hierarchy. The obtained results are in close agreement with the theoretical formulations concerning ionospheric storm effects at middle latitudes. In addition, they may be proved useful in the development of more accurate ionospheric forecasting methods.     

一种MEMS陀螺随机漂移的高精度建模方法

为补偿MEMS陀螺随机漂移,采用时间序列分析法对其进行自回归滑动平均（ARMA）模型辨识,提出一种滑动平均（MA）参数估计的新方法。先将陀螺随机漂移建模为带观测噪声的ARMA模型,在估计出自回归（AR）部分的参数后,针对AR滤波后的残差,推导出一种方差小的MA自协方差估计值,并将该估计值作为输入,利用Gevers-Wouters（GW）算法估计出MA部分的参数。仿真结果表明,MA参数估计精度得到提升的同时,参数估计可靠性也得到了增强。MEMS陀螺的随机漂移补偿实验进一步验证本文所提算法的补偿精度高于改进前。      

产品性能可靠性评估的时序分析方法

针对航空航天产品高可靠性、长寿命的特点,通过综合时序模型对随机序列自拟合性强与短期预测精度高的优点,提出了两类基于性能退化数据的产品可靠性评估时序模型方法.首先,从性能退化量分布的角度出发,在假设退化量分布类型不随时间变化的前提下,利用时间序列建立了性能退化分布参数的分析模型,进而根据可靠度与性能退化量分布的关系进行可靠性评估.然后,从退化轨迹的角度出发,对所有样本的退化轨迹进行时序分析与建模,外推伪失效区间与伪失效寿命值,进而采用完全寿命试验数据的统计方法进行可靠性评估.最后,对某金属材料疲劳裂纹扩展数据进行可靠性评估与寿命预测,结果表明该方法具有良好的稳健性.     

光纤陀螺中分形噪声的参数估计和去除

光纤陀螺零漂信号中主要存在分形噪声和高斯白噪声,采用传统的时间序列分析法很难去除这类混合噪声.基于小波分析,提出一种分步估计加性白噪声强度和分形随机过程参数的新方法.先通过拟合自相关函数,估计出白噪声的强度;再在小波变换域估计分形噪声参数,并选取适当软阈值对测量值滤波.实验结果表明该估计和降噪方法有效地去除了光纤陀螺中的混合噪声,且不需要噪声的先验知识,具有很好的适应性.      

时间序列法在临近空间大气风场预报中的应用

受多种因素影响，临近空间大气环境要素复杂多变，预报难度很大.本文采用时间序列法中的自回归滑动平均（ARMA）模型对临近空间大气风场开展统计预报方法研究，基于廊坊（39.4°N，116.7°W）中频雷达在88km高度的大气纬向风数据开展预报试验.本次预报试验的样本数据为2015年9月24日至10月24日风场数据，利用过去7天数据对未来第8天风场数据进行预报.试验结果显示，ARMA模型对临近空间大气风场预报有一定的适用性.当风场变化规律性较强，即样本数据风场呈现出比较显著的24h周期性变化时，ARMA模型预报效果较好；当风场发生突变时，预报效果变差.与实测数据的对比结果表明，ARMA模型预报结果的误差在9~27m·s-1，预报效果优于同阶自回归（AR）模型，略优于高阶AR模型.      

小波分析在GPS卫星故障检测中的应用

目前基于扩展卡尔曼滤波的残差卡方检测法已在接收机自主进行GPS卫星故障检测方面得到广泛应用,但该方法存在依赖系统数学模型、检验延迟等问题。文章提出一种基于小波分析的GPS卫星故障检测法,利用小波分析在时频域表现出良好的细节处理特性,将GPS接收机的可测数据即伪距观测数据和位置定位数据作为处理对象,进行多尺度下的分析,通过识别异常点来判断故障的发生。仿真结果表明,该方法具有高效灵敏、简洁直观、易于工程实现等特性,有助于保证导航系统的可靠性和稳定性。     

涂覆吸波材料圆柱的RCS预估方法

用缩比模型测量结果预估是研制阶段获得大尺寸目标雷达散射截面（RCS）的常用方法，但根据经典电磁相似理论，严格满足缩比条件的涂覆吸波材料缩比目标测量难以实现。针对涂覆吸波材料缩比目标的RCS预估问题，提出了采用多元对数线性回归模型的预估方法。设计了2组圆柱模型，在微波暗室中对缩比因子分别为1、2、4、8的2组模型进行了测试。在完成角度矫正等数据预处理基础上，将缩比模型RCS数据作为训练集代入模型当中求得参数，对原模型的RCS进行预估并与实际实测数据进行对比分析。结果表明:所提方法预估数据与实测数据曲线拟合度较好，相较于传统平方率模型，误差下降了3~5 dB，在回归模型中加入吸波材料因子后误差进一步下降了0.3~0.8 dB。      

光纤陀螺随机漂移模型

随机漂移是光纤陀螺的主要误差,建立数学模型在输出中补偿是抑制该项误差、提高光纤陀螺精度的有效方法.光纤陀螺静态输出为随机过程,对该随机过程的平稳性和正态性进行分析,拟合趋势项、周期项并补偿,使其成为平稳随机序列.采用时间序列分析法建立光纤陀螺随机漂移模型,根据随机漂移自相关和偏相关系数的特性辨识模型的类型和阶数,利用最小二乘方法估计模型参数,得到光纤陀螺随机漂移模型为AR(2).对陀螺输出数据补偿,检验模型的适用性.结果表明,该模型具有很好的适用性,能够有效抑制随机漂移,提高光纤陀螺精度,可以作为惯导系统卡尔曼滤波器状态变量的数学模型.      

Time-series analysis of GPS measurements for long-span bridge movements using wavelet and model prediction techniques

This study aims at assessing the safety behavior of the Incheon long-span bridge using high rate (10?Hz) geodetic monitoring global positioning system (GPS). The time series of wavelet spectrum analysis is utilized to assess the dynamic behavior of the bridge. The coefficients and model errors of the time series autoregressive-moving average (ARMA) model are used to evaluate the movement performances of the bridge. The results show that: (i) the accuracy of GPS measurements to extract the dynamic behavior of the bridge is 97.27% when compared with the design results. (ii) the behavior of the bridge is within the safety limits of the bridge design with minimum observed changes for the historical GPS measurements in time and frequency domains, the mean deflection of bridge deck is 8.26?mm and frequency changes of bridge is 0.004?Hz compared with the design results. (iii) the time series analysis of the wavelet spectrum and ARMA model coefficients can be used to detect the significant frequency changes and study the rigidity of the bridge performance, respectively; and the both methods are found to be suitable techniques to estimate the performance changes of the GPS measurements in the time and frequency domains during the monitoring time period.     

嫦娥一号CCD立体相机数据光度校正

为更加精确拟合相函数系数, 得到较为准确的光度校正结果, 针对嫦娥一号CCD立体相机数据的特点, 对改进的Lommel-Seeliger模型中的Lommel-Seeliger因子进行修订, 使去除太阳入射角和传感器观测角影响的遥感图像数据能够更好地契合相位角的变化; 利用加入修订后Lommel-Seeliger因子的模型对嫦娥一号CCD立体相机数据进行逐像素光度校正; 选取同一轨道不同纬度和同一区域不同视角两组数据来验证算法的有效性和适用性. 实验结果表明, 该方法能够有效校正由于几何观测条件变化而引起的目标光谱特性的不一致性, 对于较亮和较暗区域的光度校正效果更为有效.      

Computerized ionospheric tomography with the IRI model

Computerized ionospheric tomography (CIT) is a method to estimate ionospheric electron density distribution by using the global positioning system (GPS) signals recorded by the GPS receivers. Ionospheric electron density is a function of latitude, longitude, height and time. A general approach in CIT is to represent the ionosphere as a linear combination of basis functions. In this study, the model of the ionosphere is obtained from the IRI in latitude and height only. The goal is to determine the best representing basis function from the set of Squeezed Legendre polynomials, truncated Legendre polynomials, Haar Wavelets and singular value decomposition (SVD). The reconstruction algorithms used in this study can be listed as total least squares (TLS), regularized least squares, algebraic reconstruction technique (ART) and a hybrid algorithm where the reconstruction from the TLS algorithm is used as the initial estimate for the ART. The error performance of the reconstruction algorithms are compared with respect to the electron density generated by the IRI-2001 model. In the investigated scenario, the measurements are obtained from the IRI-2001 as the line integral of the electron density profiles, imitating the total electron content estimated from GPS measurements. It has been observed that the minimum error between the reconstructed and model ionospheres depends on both the reconstruction algorithm and the basis functions where the best results have been obtained for the basis functions from the model itself through SVD.     

改进的小波阈值消噪法应用于超声信号处理

超声检测中回波信号信噪比低、易于被噪声淹没,小波变换是一种有效的提取缺陷回波的方法.建立了超声缺陷回波信号的数学模型,对基于小波变换的软、硬阈值消噪法作了改进,提出一种折中方法用于超声缺陷回波信号的去噪,同时以信噪比为目标函数对参数的选取也作了优化.仿真实验结果表明,改进方法非常适合用于超声信号的分析,能够很好地抑制噪声,它最大程度的发挥了小波软、硬阈值消噪法的优点,避免它们的缺点,使用该方法处理的信号相对于小波软、硬阈值消噪法在一定程度上改善了去噪的效果,提高了回波信号的信噪比.      

Universal scaling laws for fully-developed magnetic field turbulence near and far upstream of the Earth’s bow shock

We analyze the multifractal scaling of the modulus of the interplanetary magnetic field near and far upstream of the Earth’s bow shock, measured by Cluster and ACE, respectively, from 1 to 3 February 2002. The maximum order of the structure function is carefully estimated for each time series using two different techniques, to ensure the validity of our high-order statistics. The first technique consists of plotting the integrand of the pth order structure function, and the second technique is a quantitative method which relies on the power-law scaling of the extreme events. We compare the scaling exponents computed from the structure functions of magnetic field differences with the predictions obtained by the She–Lévêque model of intermittency in anisotropic magnetohydrodynamic turbulence. Our results show a good agreement between the model and the observations near and far upstream of the Earth’s bow shock, rendering support for the modelling of universal scaling laws based on the Kolmogorov phenomenology in the presence of sheet-like dissipative structures.     

A time-frequency analysis of the thermal radiation background anomalies caused by large earthquakes: A case study of the Wenchuan 8.0 earthquake

Strong earthquakes have an impact on the regional thermal radiation background, which has been both observed and confirmed. This effect produces anomalies in the thermal radiation background (TRBA) and increases the difficulty of extracting a thermal radiation anomaly (TRA) that is associated with an earthquake occurring during the same time period. The extraction and identification of such anomalies has been ignored by previous studies. In this study, we investigate the time-frequency analysis (TFA) method, together with the wavelet filtering of the Daubechies method and the relative power spectrum analysis of the Fourier Welch method to extract and analyse the TRBA caused by the 2008 Wenchuan Ms 8.0 earthquake and the TRA of the 2013 Minxian-Zhangxian Ms 6.6 earthquake using data concerning the brightness temperature of a black body (TBB) from the Fengyun-2 series of geostationary meteorological satellites developed by China. The result showed that this method can effectively extract and analyse the TRBA caused by the Wenchuan earthquake and the TRA of the Minxian-Zhangxian earthquake form a complex background environment. Furthermore, we discussed the impact of the earthquake on the TRBA and segmented the process. The impact is mainly reflected by three aspects; the characteristic period of the TRBA changes, the TRBA occurs at the same time every year, which is identical to the time at which the earthquake anomaly occurred, and the impact process is in stages. We also summarized the correlation between the characteristic parameters of a TRA and the regional thermal radiation background, geography, and climatic factors.     

基于LSTAR的机载燃油泵多阶段退化建模

机载燃油泵的性能退化呈现出平稳—加速—平稳的非线性、多阶段模式,针对现有退化模型难以准确描述其全寿命周期性能退化的问题,以逻辑平滑转换自回归(LSTAR)模型为工具,对机载燃油泵出口压力传感器信号进行建模。首先,对转换后的压力传感器信号建立自回归(AR)模型,通过非线性检验说明建立LSTAR模型的必要性;然后,应用非线性最小二乘法完成参数估计;最后,在AIC准则最小及拟合优度最大的原则下,选择转换变量,通过残差进行模型的适应性检验与正态性检验。结果表明:基于LSTAR模型的拟合精度明显优于线性自回归模型。本文提出的方法成功解决了机载燃油泵性能退化的多阶段准确建模问题,为机载燃油泵的预测与健康管理(PHM)奠定了坚实的基础。     

An empirical model for the variation of rain drop size distribution with rain rate at a few locations in southern India

A Joss–Waldvogel impact type disdrometer was installed at four different locations in the Indian peninsula during various periods from 2001 till date. The data are analysed to study the nature of rain drop size distribution (DSD) in this region. Out of the three well known distributions that describe DSD, namely, the Marshall–Palmer, Gamma and Lognormal, it has been found that Lognormal distribution fits the DSD in this region better than the other ones. Lognormal distributions for different rain rates were then derived by fitting the lognormal function to the data using a curve fitting software. Then the variation of fit parameters with rain rate was evaluated. Incorporating these variations, into the Lognormal distribution, an empirical equation that describes the DSD in this region for different rain rates was derived. Then this equation was tested with sample data from each of these stations. The data used for validation were not used for fitting lognormal equation to derive the fit parameters. The correlation between the DSD measured and derived using the empirical model was found to be quite good (0.9) except in some cases where the coefficient dropped to 0.75. The empirical model can be updated when more data are available.     

Effects of dust polarity and nonextensive electrons on the dust-ion acoustic solitons and double layers in earth atmosphere

Propagation of dustion acoustic solitary waves (DIASWs) and double layers is discussed in earth atmosphere, using the Sagdeev potential method. The best model for distribution function of electrons in earth atmosphere is found by fitting available data on different distribution functions. The nonextensive function with parameter $q=0.58$ provides the best fit on observations. Thus we analyze the propagation of localized waves in an unmagnetized plasma containing nonextensive electrons, inertial ions, and negatively/positively charged stationary dust. It is found that both compressive and rarefactive solitons as well as double layers exist depending on the sign (and the value) of dust polarity. Characters of propagated waves are described using the presented model.