组合导航抗差自适应卡尔曼滤波

针对组合导航系统的定位精度与稳定性要求不断提高的现状,该文引入一种观测噪声协方差与抗差自适应相结合的Kalman滤波算法。利用新息向量和移动窗口协方差分析法,动态自适应修正观测噪声协方差阵;通过分析基于状态不符值、方差分量的统计量构造的自适应因子所存在的问题,提出一种由预测残差向量构造的自适应因子。仿真结果表明,该方法能够有效抑制观测异常对组合导航定位精度的影响。     

顾及频率间偏差的GPS/GLONASS卫星钟差实时估计

GPS/GLONASS卫星钟差联合估计过程中,由于GLONASS系统采用频分多址技术区分卫星信号,因而会产生频率间偏差（IFB）^[1]。本文在GPS/GLONASS卫星定轨过程中的IFB参数特性分析的基础上,引入IFB参数,实现顾及频率间偏差的GPS/GLONASS卫星钟差实时估计。同时,为解决实时估计中待估参数过多导致的实时性较弱等问题,基于非差伪距观测值和历元间差分相位观测值改进实时估计数学模型,实现多系统卫星钟差的联合快速估计。结果表明：GPS/GLONASS联合估计时需引入IFB参数并优化其估计策略,采用MGEX和iGMAS跟踪站的实测数据进行实时钟差解算,快速估计方法可实现1.6 s逐历元快速、高精度估计,与GBM提供的最终精密卫星钟差相比,GPS卫星钟差实时精度约为0.210 ns,GLONASS卫星约为0.298 ns。     

基于小波降噪的Kalman滤波预报卫星钟差

卫星钟差预报在实时高精度导航定位中具有重要作用,Kalman滤波模型是预报卫星钟差的重要方法之一。为了进一步提高Kalman滤波模型预报卫星钟差的精度,本文提出了基于小波降噪的Kalman滤波模型预报卫星钟差。该模型使用小波降噪后数据,在保留Kalman滤波模型特点的基础上,明显地提高了短期卫星钟差预报精度。     

Real-time clock offset prediction with an improved model

The GPS orbit precision of the IGS ultra-rapid predicted (IGU-P) products has been remarkably improved since 2007. However, the satellite clock offsets of the IGU-P products have not shown sufficient high-quality prediction to achieve sub-decimeter precision in real-time precise point positioning (RTPPP), being at the level of 1–3 ns (30–90 cm) RMS in recent years. An improved prediction model for satellite clocks is proposed in order to enhance the precision of predicted clock offsets. First, the proposed prediction model adds a few cyclic terms to absorb the periodic effects, and a time adaptive function is used to adjust the weight of the observation in the prediction model. Second, initial deviations of the predictions are reduced by using a recomputed constant term. The simulation results have shown that the proposed prediction model can give a better performance than the IGU-P clock products and can achieve precision better than 0.55 ns (16.5 cm) in real-time predictions. In addition, the RTPPP method was chosen to test the efficiency of the new model for real-time static and kinematic positioning. The numerical examples using the data set of 140 IGS stations show that the static RTPPP precision based on the proposed clock model has been improved about 22.8 and 41.5 % in the east and height components compared to the IGU-P clock products, while the precisions in the north components are the equal. The kinematic example using three IGS stations shows that the kinematic RTPPP precision based on the proposed clock model has improved about 30, 72 and 44 % in the east, north and height components.     

BDS卫星实时钟差估计

随着无人驾驶等高新技术的快速发展，实时精密单点定位在GNSS领域中受到越来越多的关注。研究实时卫星钟差的获取和实时定位精度具有较大的现实意义，本文为研究耦合BDS卫星轨道、钟差产品对定位精度的影响，采用不同精度的轨道产品实时获取卫星钟差。分析了卫星钟差误差与轨道误差之间的相关性及钟差对轨道误差的吸收能力，发现卫星钟差能够吸收95%以上的轨道径向误差和部分切线误差，在一定程度上弥补了轨道误差引起的定位误差。采用耦合的卫星轨道、钟差产品，单BDS系统定位精度可达到分米级的定位结果。     

Comparison of Two Fading Filters anc Adaptively Robust Filter

Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.     

Comparison of two fading filters and adaptively robust filter

Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.     

基于噪声估计的卡尔曼滤波用于GPS卫星钟差预报研究

利用重叠哈达玛方差确定卫星钟噪声随机模型,采用顾及钟差随机噪声模型的卡尔曼滤波进行钟差预报分析,并与最小二乘预报算法相比较,得出以下结论:卡尔曼滤波进行1 d以内的短期预报时,精度达到亚纳秒级,优于最小二乘预报算法,在长期预报或拟合数据量较少时,最小二乘预报精度优于卡尔曼滤波。     

实时卫星钟差基准精化方法

在基于精密单点定位(PPP)的授时方法中,卫星钟差产品的高精度时间基准至关重要.针对实时卫星钟差产品时间基准不够稳定的问题,本文采用一组具有原子钟外部输入的国际全球卫星导航系统(GNSS)服务(IGS)跟踪站建立了顾及原子钟变化特性的基准精化方法.该方法首先采用阿伦方差对不同的IGS跟踪站外接原子钟进行稳定度分析,挑选出一组稳定度高的原子钟用以精化时间基准.在此基础上,利用阿伦方差分析各台原子钟的噪声参数特征,并确定不同原子钟之间的权比关系.最终,建立时间基准改正量的随机模型,并计算出精化后的时间基准.通过实例验证表明:与IGS事后精密钟差产品定义的时间基准比较,改正后的实时钟差基准单天内的标准差(STD)优于0.1 ns,相比于改正前最高提升了93%.同时,基准改正后的天内万秒稳达到10^-15量级,实现了一个量级的提高.此外,通过相对钟差精度的分析,表明钟差基准修正不影响PPP的定位精度.     

结合卡尔曼滤波的城市路段速度估计

本文以城市出租车为浮动车数据采集源,介绍了基于GPS数据的实时路段速度估计的基本方法。针对目标路段GPS数据样本量不足的情况,考虑邻近区域的路段速度、上周同日速度、前一时刻速度等与目标路段当前时刻速度等密切相关的变量,建立多元线性回归方程,利用卡尔曼滤波融合预测值和测量值,从而提高路段行驶速度的估计精度。选择广州市东风路作为测试实例,融合值比测量值误差降低9%,绝对相对误差变动系数减少4%,表明结合卡尔曼滤波技术的城市路段速度估计精度和稳定性均得到提高。     

几种精密卫星钟差加密方法的比较与分析

卫星钟差是影响GPS高精度单点定位的一个重要因素,通过分析几种加密GPS精密卫星钟差的方法,将加密结果与GFZ(German Research Centre for Geosciences)提供的数据进行比较,并通过精度分析,得出分段线性插值法是加密GPS精密卫星钟差一种较为可靠的方法。     

大气可降水量预测模型的自适应Kalmam滤波改进

针对现有可降水量预报模型存在预报精度不高等问题,该文提出采用方差分量估计自适应卡尔曼滤波对可降水量数据进行预处理,用以提高径向基神经网络预测模型的预测精度,从而形成高精度预报模型。通过比较不同基站不同时间的数据,分析使用方法的预报精度。实验结果表明:将预测模型应用于全国7个测站进行实验,预测相对精度的平均值可达95%以上,预报残差在10-5左右,残差值小于0.001的占90%以上。在影响因素方面,使用较短时间作为模型原始数据进行预测会得到较好的预测结果。实验证明本预测方法在预报大气可降水量值方面具有较高的精度。     

Adaptive estimation of multiple fading factors in Kalman filter for navigation applications

Kalman filter is the most frequently used algorithm in navigation applications. A conventional Kalman filter (CKF) assumes that the statistics of the system noise are given. As long as the noise characteristics are correctly known, the filter will produce optimal estimates for system states. However, the system noise characteristics are not always exactly known, leading to degradation in filter performance. Under some extreme conditions, incorrectly specified system noise characteristics may even cause instability and divergence. Many researchers have proposed to introduce a fading factor into the Kalman filtering to keep the filter stable. Accordingly various adaptive Kalman filters are developed to estimate the fading factor. However, the estimation of multiple fading factors is a very complicated, and yet still open problem. A new approach to adaptive estimation of multiple fading factors in the Kalman filter for navigation applications is presented in this paper. The proposed approach is based on the assumption that, under optimal estimation conditions, the residuals of the Kalman filter are Gaussian white noises with a zero mean. The fading factors are computed and then applied to the predicted covariance matrix, along with the statistical evaluation of the filter residuals using a Chi-square test. The approach is tested using both GPS standalone and integrated GPS/INS navigation systems. The results show that the proposed approach can significantly improve the filter performance and has the ability to restrain the filtering divergence even when system noise attributes are inaccurate.     

Application of unscented Kalman filter in satellite orbit simulation

A new estimate method is proposed, which takes advantage of the unscented transform method, thus the true mean and covariance are approximated more accurately. The new method can be applied to nonlinear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what's more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of satellite orbit simulation. Numerical experiments show that the application of the unscented Kalman filter is more effective than the EKF.     

Application of unscented Kalman filter in satellite orbit simulation

IntroductionAs is well known,the Kal manfilter(KF) is al-ways usedto deal withthe system whose dynam-ics and observation models are linear , and theextended Kal manfilter(EKF) is the most widelyused esti mator for nonlinear systems . In theEKFthe kal man …     

局部特征匹配的卫星影像几何偏差估计可行性分析

几何定位偏差是评估卫星影像质量的重要参数之一。本文利用特征匹配方法进行卫星影像几何偏差估计,并对该方法的可行性进行分析。首先,选取包含典型地物的局部卫星影像构建路标基准影像库;其次,采用局部点特征描述子对卫星影像与路标基准影像库进行亚像素提取与匹配,确定同名像点,然后为提升同名像点匹配精度,提出了顾及空间关系与几何一致性约束的匹配策略,继而,对卫星影像与路标基准影像库的匹配像点进行内插计算,获取同名像点的地理空间坐标;最后,通过差值计算得到卫星影像相对路标基准影像的偏移参数,完成几何偏差估计和可行性分析。本文以我国风云卫星为例,选取包含海岸线、湖泊、山脉、河流及岛屿的局部卫星影像集构建路标基准影像库,并选用SIFT、SURF、ORB 3个局部特征进行测试分析。测试结果表明,基于局部特征匹配策略对卫星影像几何偏差估计的系统误差小于0.1像素,定量地证明了局部特征匹配方法对卫星影像几何偏差估计的可行性。     

The stochastic estimation of satellite clock correction information in WADGPS

1 IntroductionChnventional DGPS is limited by the range overwhich the differential corrections are valid due tothe rapid decorrelation of the error sources with in-creasing distance from the reference station to user.In wide area differential GPS (WWPS) errorsources in GPS measurements are medeled sepa-rately,on the basis of a limited number of referencestaitOns, to overcome this drawback. The main er-ror sources are regarded as broadcast ephemeris er-ror,atmospheric refraction and satel…     

提高卫星导航精度的卡尔曼滤波算法应用研究

采用卫星导航对海上航行的船舶进行速度测量是目前最广泛应用的方法,但是卫星导航接收机由于受外界偶然因素的影响,其显示的速度往往包含噪声,且属于随机的高斯白噪声。针对此问题,提出一种基于卡尔曼滤波理论的计算模型,只考虑上一个时刻和当前时刻的关系,大大地减少数据冗余;应用迭代的方法来处理卡尔曼滤波,以更好地简化计算的过程,并根据深圳某航海公司的实际数据对此计算模型进行仿真验证。实验结果表明,对于数据跳跃十分大且明显的噪声数据,经过滤波后,这些数据变得更加平滑和准确。     

自适应卡尔曼滤波在GPS/DR组合导航中的应用

针对在观测条件较差的情况下卡尔曼滤波的鲁棒性较差,本文设计了一种自适应卡尔曼滤波模型。通过实际车载GPS/DR组合导航试验,结果表明该模型在观测质量较差的情况下能够抑制较大的偏差,相对于标准卡尔曼滤波模型,其平面定位精度提高了近一倍,达到2～3m。因此,在观测环境较差的情况下建议采用渐消自适应卡尔曼滤波模式进行组合导航。     

基于状态矩阵卡尔曼滤波的姿态估计算法研究

设计一种组合GPS／速率陀螺定姿系统。系统以方向余弦矩阵表示姿态,建立GPS／速率陀螺组合状态模型和观测模型。结合kalman滤波算法,提出一种状态矩阵卡尔曼滤波（StateMatrixKalmanKilter,SMKF）姿态估计算法,并采用拉格朗日算法对姿态矩阵进行正交化约束。与传统的基于四元数的扩展卡尔曼滤波（EKF）算法相比,基于方向余弦矩阵的姿态系统状态方程与测量方程均为线性方程,无需线性化处理,对初始姿态误差更具有较好的鲁棒性。数值仿真表明,该方法具有精度高和稳定性强等优点。