利用空间目标监测数据的大气质量密度模型校正

正绕地运行空间目标精密轨道确定的基本前提是精确的摄动力模型和高精度、高密度观测数据。随着地球重力场的精确模型化,许多配置全球卫星导航系统等先进跟踪设施的卫星的精密轨道确定已成为常态。但是,对于数以十万计的空间碎片而言,由于大气质量密度模型的较大误差和监测数据的稀疏性,低轨碎片几天的轨道预报误差便可达数千米。越来越多卫星的入轨运行,使空间碰撞预警、碎片规避、碎片清理等空间应用对空间目标轨道(特别是轨道预报)精度提出了百米甚至十米量级的要求。空间目标轨道数据已成为航天和国防安全的基础信息之一,更是空间态势感知(SSA)的核心要素之一。     

基于方差递推法的Kalman滤波在钟差预报中的应用

卫星导航系统中星载原子钟的钟差预报对于导航、定位及授时具有重要的作用。为了提高卫星钟差预报的精度,设计了一种两步确定卫星钟噪声协方差矩阵的Kalman滤波钟差预报模型。该方法首先基于Hadamard总方差确定卫星钟噪声协方差矩阵的初值,然后,使用方差递推法得到滤波过程中卫星钟的噪声协方差矩阵。使用GPS系统的星载铷钟数据进行短期预报,并与常用的二次多项式模型、灰色模型进行对比,结果表明:本文中提出的方法可以实现高精度的卫星钟差预报且预报效果优于两种常用模型,同时,该方法能够在一定程度上弥补预报误差随预报时间增加而不断变大的不足。     

一种基于少通道准校正GNSS干扰信号的空间谱测向方法

针对导航干扰信号测向初始相位估计难度大导致空间谱测向精度低、误差大的问题,提出了一种以经典多重信号分类(MUSIC)算法为基础的“少通道准校正空间谱测向方案”. 该方案是在每个通道内加入校准源,从而获得任意两个通道的初始相位差,减小了通道间的初始相位差对协方差矩阵计算的影响,实现空间谱测向的目的. 将所提的方案应用于五阵元三通道测向天线并在试验场地进行测试,实验结果表明:该方法能够解决复杂场景下单信号、双信号的测向,在低信噪比(SNR)的情况下,测向误差仍能保持在3.5°左右,测向精度较高,测向结果与全通道的测向结果基本一致.      

影像畸变对基本矩阵估计精度的影响分析

由于镜头制造工艺的限制和拍摄条件及环境的影响,影像本身都存在或多或少的畸变误差,这种误差通过传播和累积给影像的后期处理及各种应用造成了不稳定因素。针对计算机立体视觉中基本矩阵的估计精度问题进行了深入研究,通过对相机非线性畸变的自动纠正,分别获取了校正前和校正后的基本矩阵。实验数据表明,在对影像进行畸变校正后估计出来的基本矩阵在平均核线距离和平均余差上都明显小于畸变校正前的。     

神经网络在北斗导航卫星轨道预报中的应用

导航卫星的自主定轨是提高卫星导航系统生存能力的一个重要手段,在解决导航星座自主定轨中涉及到高精度的轨道预报,提高轨道预报精度对于自主定轨精度有着重要意义。针对利用动力学模型得到的预报轨道随时间推移精度衰减较快的问题,本文提出了一种改进北斗导航卫星中长期轨道预报精度的新方法。利用神经网络作为建立预报模型的工具,在动力学模型的基础上建立神经网络模型,通过对历史时刻预报误差的学习及训练,掌握其变化规律,再用于补偿和改进当前时刻的预报轨道,以达到提高预报精度的目的。本文制定了导航卫星轨道中长期预报方案,并利用实测数据进行了实验分析,结果表明,采用神经网络模型补偿预报轨道误差时,不同卫星在不同初始时刻下的改进效果是不同的。预报15d导航卫星的轨道精度由318m提高至19m,预报30d轨道精度由1 757m提高至49m。预报15d、30d轨道改进幅度分别为41%~80%、32%~88%。     

影像畸变对基本矩阵估计精度的影响分析

由于镜头制造工艺的限制和拍摄条件及环境的影响,影像本身都存在或多或少的畸变误差,这种误差通过传播和累积给影像的后期处理及各种应用造成了不稳定因素.针对计算机立体视觉中基本矩阵的估计精度问题进行了深入研究,通过对相机非线性畸变的自动纠正,分别获取了校正前和校正后的基本矩阵.实验数据表明,在对影像进行畸变校正后估计出来的基本矩阵在平均核线距离和平均余差上都明显小于畸变校正前的.     

卫星导航系统完备性指标SISA的计算和分析

根据GALILEO系统的完备性指标概念,采用基于轨道误差和卫星钟差协方差矩阵计算完备性指标的方法,利用GPS星历数据计算和分析了空间信号误差(SISE)和完备性指标(SISA)的变化和分布情况.结果表明,GPS卫星各分量中误差相差较大,对于不同的卫星应分别计算其完备性指标;不同GPS卫星的SISE随时间的变化幅度不同,呈明显的随机性;在中国区域内,SISA的变化具有一定的规律性,在低纬高经度地区SISA具有较小的数值,在同一高纬地区SISA的数值相近;针对GPS系统的SISA计算结果,GALILEO系统需大幅提高轨道和钟差预报精度才能满足生命安全服务要求.     

卫星导航系统完备性指标SISA的计算和分析

根据GALILEO系统的完备性指标概念,采用基于轨道误差和卫星钟差协方差矩阵计算完备性指标的方法,利用GPS星历数据计算和分析了空间信号误差(SISE)和完备性指标(SISA)的变化和分布情况。结果表明,GPS卫星各分量中误差相差较大,对于不同的卫星应分别计算其完备性指标;不同GPS卫星的SISE随时间的变化幅度不同,呈明显的随机性;在中国区域内,SISA的变化具有一定的规律性,在低纬高经度地区SISA具有较小的数值,在同一高纬地区SISA的数值相近;针对GPS系统的SISA计算结果,GALILEO系统需大幅提高轨道和钟差预报精度才能满足生命安全服务要求。     

GPS精密轨道插值误差及方差估计

分析了影响GPS精密轨道插值传播误差精度的因素,给出了IGS精密星历误差在多项插值中的传播模型及传播误差的方差分量计算方法,为评定GPS插值轨道的精度提供了量化的客观依据,并用算例证明了卫星插值轨道的传播误差精度与精密星历误差精度在同一量级。     

自适应抗差KF-UKF的超宽带导航定位方法

针对超宽带导航定位中量测信息异常误差和非线性滤波问题,该文提出了一种基于自适应抗差卡尔曼滤波-无迹卡尔曼滤波(KF-UKF)的超宽带导航定位算法。该算法首先利用卡尔曼滤波计算预测状态向量及其协方差矩阵,利用无迹卡尔曼滤波进行量测更新;然后利用先验阈值和预测残差构建量测噪声的抗差协方差矩阵,以减少量测信息异常误差的影响,同时利用自适应因子对算法进行调节和修正。结果表明,该算法能有效地抑制并消除超宽带测距中量测信息异常误差的影响,能有效地处理状态模型误差的影响,提高超宽带导航定位的精度和稳定性,同时拥有比无迹卡尔曼滤波算法更高的计算效率。     

轨道误差对InSAR 数据处理的影响

轨道参数是InSAR 数据处理中一个重要的参数,对从最初的图像配准到最后的高程值或形变值图像生成都有着重要的影响.含有误差的轨道参数造成基线误差以残差条纹的形式存在于干涉图中.完全去除残差条纹要求轨道绝对精度低于1mm,目前的定轨精度远不能满足要求.这里推导了轨道误差和残差干涉条纹的关系,分析了轨道误差对高程值和形变值影响的特点,提出采用二次多项式拟合的方法去除残差干涉条纹,并以巴姆地区的 ENVISAT 数据证明了提出方法的有效性.     

随机变量非线性变换统计性质的高精度逼近算法

应用扩展卡尔曼滤波对非线性系统进行状态估计时 ,要对系统的动力方程进行线性化 ,这就为状态的估计带来一定的误差。本文首先对非线性变换的函数进行级数展开 ,获得了随机变量经非线性变换后的真实均值和协方差表达式 ,并得到一阶线性化的均值和协方差 ,然后提出了一种精度更高的变换算法用以逼近非线性变换后的真实均值和协方差 ,理论分析和数值试验都表明新算法不仅具有更高的精度 ,而且更容易实现     

Geoid and high resolution sea surface topography modelling in the mediterranean from gravimetry,altimetry and GOCE data: evaluation by simulation

The determination of local geoid models has traditionally been carried out on land and at sea using gravity anomaly and satellite altimetry data, while it will be aided by the data expected from satellite missions such as those from the Gravity field and steady-state ocean circulation explorer (GOCE). To assess the performance of heterogeneous data combination to local geoid determination, simulated data for the central Mediterranean Sea are analyzed. These data include marine and land gravity anomalies, altimetric sea surface heights, and GOCE observations processed with the space-wise approach. A spectral analysis of the aforementioned data shows their complementary character. GOCE data cover long wavelengths and account for the lack of such information from gravity anomalies. This is exploited for the estimation of local covariance function models, where it is seen that models computed with GOCE data and gravity anomaly empirical covariance functions perform better than models computed without GOCE data. The geoid is estimated by different data combinations and the results show that GOCE data improve the solutions for areas covered poorly with other data types, while also accounting for any long wavelength errors of the adopted reference model that exist even when the ground gravity data are dense. At sea, the altimetric data provide the dominant geoid information. However, the geoid accuracy is sensitive to orbit calibration errors and unmodeled sea surface topography (SST) effects. If such effects are present, the combination of GOCE and gravity anomaly data can improve the geoid accuracy. The present work also presents results from simulations for the recovery of the stationary SST, which show that the combination of geoid heights obtained from a spherical harmonic geopotential model derived from GOCE with satellite altimetry data can provide SST models with some centimeters of error. However, combining data from GOCE with gravity anomalies in a collocation approach can result in the estimation of a higher resolution geoid, more suitable for high resolution mean dynamic SST modeling. Such simulations can be performed toward the development and evaluation of SST recovery methods.     

GLONASS卫星位置的动力学改进算法

为了减弱由于广播星历拟合引起的误差,本文提出了一种基于动力学轨道改进的GLONASS卫星位置算法。该算法仅采用GLONASS广播星历信息就能够提高GLONASS卫星位置坐标的计算精度。算例表明,该方法能够有效减弱GLONASS卫星广播星历的拟合误差,卫星位置的精度损失从0.5m降到mm级;外推2-3h,精度损失也仅在cm级水平。     

VLBI在GEO卫星精密定轨中的应用

影响GEO轨道确定精度的原因主要有两方面:一是高轨卫星的几何跟踪条件受到局部测轨网的限制;二是卫星相对于地面的动力学约束信息较弱。利用一个针对GEO卫星的简化动力法定轨的协方差分析模型,研究了联合测距跟踪网和甚长基线干涉测量(VLBI)对定轨精度的改善情况。指出测距系统的校正误差是常规测距跟踪网定轨精度的主要误差源;当附加一条东西向VLBI基线时,仅利用不定期的少量VLBI高精度数据就能够显著改善测距偏差对轨道的影响,从而保证了卫星的整体位置解算精度。     

Gibbs sampler for computing and propagating large covariance matrices

The use of sampling-based Monte Carlo methods for the computation and propagation of large covariance matrices in geodetic applications is investigated. In particular, the so-called Gibbs sampler, and its use in deriving covariance matrices by Monte Carlo integration, and in linear and nonlinear error propagation studies, is discussed. Modifications of this technique are given which improve in efficiency in situations where estimated parameters are highly correlated and normal matrices appear as ill-conditioned. This is a situation frequently encountered in satellite gravity field modelling. A synthetic experiment, where covariance matrices for spherical harmonic coefficients are estimated and propagated to geoid height covariance matrices, is described. In this case, the generated samples correspond to random realizations of errors of a gravity field model. AcknowledgementsThe authors are indebted to Pieter Visser and Pavel Ditmar for providing simulation output that was used in the GOCE error generation experiments. Furthermore, the NASA/NIMA/OSU team is acknowledged for providing public ftp access to the EGM96 error covariance matrix. The two anonymous reviewers are thanked for their valuable comments.     

State transformation extended Kalman filter for GPS/SINS tightly coupled integration

Extended Kalman filter (EKF) is a widely used estimator for integrated navigation systems, and it works well in general situations. However, in adverse conditions such as partially observable environments and highly dynamic maneuvers, the performance of the traditional EKF-based strap-down inertial navigation system (SINS)/GPS integrated navigation system is easily to be affected by the dynamic changes of the specific force, thus leading to the problem of error covariance inconsistency. Though the inconsistency problem can be overcome to some extent if the system matrix, the states and the error covariance matrix are propagated as fast as possible in the SINS calculation rate, the problem cannot be fully solved. State transformation extended Kalman filter (ST-EKF) mechanization, with a new converted velocity error model for the SINS, is proposed, which can also be used to solve the inconsistency problem. In the ST-EKF, the specific force vector in the system error model is replaced by the nearly constant gravity vector for local navigation. Since the propagation and the updating of the ST-EKF can be executed simultaneously in the updating interval, the computation cost is greatly reduced compared with the traditional EKF. Experiments for the GPS/SINS tightly coupled navigation, including linear vibration Monte Carlo test and an unmanned aerial vehicle flight test, are implemented to evaluate the performance of the proposed ST-EKF. The results show that the proposed ST-EKF has superior performance to the traditional EKF, especially in partially observable situations.     

Efficient interpolations to GPS orbits for precise wide area applications

For precise real time or near real time differential GPS positioning in a wide or global area, precise GPS orbits or, alternatively, precise orbital corrections with respect to a reference orbit, such as GPS broadcast ephemerides, must be used. This work tests orbit interpolation methods, in order to represent the GPS orbits and orbital corrections accurately and efficiently for these and other GPS applications. For precise GPS orbits given in the SP3 format at the 15 min interval, numerical tests were conducted using Lagrange and Chebyshev as well as trigonometric polynomial functions. The results have demonstrated that the 19- or 20-term trigonometric function is apparently the most efficient interpolator for a 12 h GPS orbital arc, achieving 1 cm level 3D interpolation accuracy that can meet the requirements of most precise applications. The test results also demonstrated that the 9-term trigonometric function always yields optimal interpolation for a 2 h GPS orbit arc, in terms of interpolation errors, compared to the results when using a different number of terms for the same function or one of the other tested polynomial functions. This is evident from the minimal performance degradation when using the 9-term trigonometric function to interpolate near or at the end of a data interval. By limiting interpolation to the center 15 min to 1.5 h of a 2 h orbit arc, thereby eliminating the need to interpolate near the ends of that interval, users can opt for more terms (11 and 13) or different interpolators to further improve interpolation accuracy. When interpolating the orbital corrections with respect to the GPS broadcast ephemeris, all the tested interpolation functions of 3- to 9-term yield the same suitably accurate results. Therefore, a 3- to 5-term trigonometric function is arguably sufficiently accurate and more efficient for GPS orbital correction messaging in wide area and real time positioning.     

The impact of accelerometry on CHAMP orbit determination

 The contribution of the STAR accelerometer to the CHAMP orbit precision is evaluated and quantified by means of the following results: orbital fit to the satellite laser ranging (SLR) observations, GPS reduced-dynamic vs SLR dynamic orbit comparisons, and comparison of the measured to the modeled non-gravitational accelerations (atmospheric drag in particular). In each of the four test periods in 2001, five CHAMP arcs of 2 days' length were analyzed. The mean RMS-of-fit of the SLR observations of the orbits computed with STAR data or the non-gravitational force model were 11 and 24 cm, respectively. If the accelerometer calibration parameters are not known at least at the few percent level, the SLR orbit fit deteriorates. This was tested by applying a 10% error to the along-track scale factor of the accelerometer, which increased the SLR RMS-of-fit on average to 17 cm. Reference orbits were computed employing the reduced-dynamic technique with GPS tracking data. This technique yields the most accurate orbit positions thanks to the estimation of a large number of empirical accelerations, which compensate for dynamic modeling errors. Comparison of the SLR orbits, computed with STAR data or the non-gravitational force model, to the GPS-based orbits showed that the SLR orbits employing accelerometer observations are twice as accurate. Finally, comparison of measured to modeled accelerations showed that the level of geomagnetic activity is highly correlated with the atmospheric drag model error, and that the largest errors occur around the geomagnetic poles. Received: 7 May 2002 / Accepted: 18 November 2002 Correspondence to: S. Bruinsma Acknowledgments. The TIGCM results were obtained from the CEDAR database. This study was supported by the Centre National d'Etudes Spatiales (CNES). The referees are thanked for their helpful remarks and suggestions.     

DEM线性建模过程中的高程误差传播

在建立数字高程模型(DEM)时,基于规则格网数据和基于三角形网数据的线性建模是常用的方法。在建模过程中,结点的高程误差会随之传播。研究了DEM在基于规则格网数据和基于三角形网数据的线性建模过程中的高程误差传播问题,推导出了在结点高程误差相关情形下的高程误差传播公式,求出了在规则格网和不规则格网面上的平均高程传播误差。平均高程传播误差可以作为DEM线性插值过程中高程误差传播的度量指标。