不同类型识别变量的自回归模型异常值探测的Bayes方法

讨论基于自回归模型(AR模型)的时间序列数据中异常值探测的Bayes方法。该方法针对自回归模型引入不同类型的识别变量,通过比较这些识别变量的后验概率值与事先给定的阈值来进行异常值定位;基于Gibbs抽样算法,提出识别变量后验概率值的计算方法和异常值的估算方法;进行了大量的模拟试验并把该方法应用于卫星钟差实测数据的异常值探测,结果表明,该方法对于解决时间序列数据中在同一时刻或不同时刻出现加性异常值或革新异常值的探测问题是可行的和有效的。     

一种基于小波变换的GPS基线求解算法

在分析ＧＰＳ相位观测粗差,周跳及随机噪声的小波变换特性基础上,提出了基于小波变换的ＧＰＳ相位粗差探测及小波滤波算法,并提出了基于波滤波的ＧＰＳ基线求解算法,该方法能缩小模糊度搜索空间,提高整数模糊度解的有效性。     

海上动态GNSS浮标的周跳处理算法

常规全球卫星导航系统（GNSS）周跳探测方法大多忽略了高度角对多路径误差以及观测噪声的影响．由于海上GNSS浮标的数据质量受海面影响很大,在卫星高度角降低时其观测噪声和多路径误差显著增大,且具有高频周跳特点,常规GNSS周跳探测方法并不适用．据此,提出了一种综合电离层总电子含量变化率（TECR）和顾及高度角加权阈值模型的改进双频码相组合（MW）探测法的周跳探测与修复方法．实验结果表明:该方法能有效抑制低高度角和多路径影响带来的信号噪声,准确探测到各种类型周跳,可有效应用于海上GNSS浮标的数据预处理.      

一种基于场论的空间异常探测方法

从空间数据场的角度,借鉴高斯势函数发展了一种新的空间异常度度量指标。进而,提出了一种基于场论的空间异常探测方法。该方法通过空间聚类获得局部相关性较强的空间簇,并构建合理、稳定的空间邻近域。在此基础上,采用专题属性变化梯度修复策略减弱空间邻近域中潜在异常的影响,并利用空间异常度度量指标计算实体的异常度,从而探测空间异常。实验结果及实例证明了此方法的正确性。     

The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem

This paper deals with the problem of detecting and correcting cycle-slips in Global Navigation Satellite System (GNSS) phase data by exploiting the Bayesian theory. The method is here applied to undifferenced observations, because repairing cycle-slips already at this stage could be a useful pre-processing tool, especially for a network of permanent GNSS stations. If a dual frequency receiver is available, the cycle-slips can be easily detected by combining two phase observations or phase and range observations from a single satellite to a single receiver. These combinations, expressed in a distance unit form, are completely free from the geometry and depend only on the ionospheric effect, on the electronic biases and on the initial integer ambiguities; since these terms are expected to be smooth in time, at least in a short period, a cycle-slip in one or both the two carriers can be modelled as a discontinuity in a polynomial regression. The proposed method consists in applying the Bayesian theory to compute the marginal posterior distribution of the discontinuity epoch and to detect it as a maximum a posteriori (MAP) in a very accurate way. Concerning the cycle-slip correction, a couple of simultaneous integer slips in the two carriers is chosen by maximazing the conditional posterior distribution of the discontinuity amplitude given the detected epoch. Numerical experiments on simulated and real data show that the discontinuities with an amplitude 2 or 3 times larger than the noise standard deviation are successfully identified. This means that the Bayesian approach is able to detect and correct cycle-slips using undifferenced GNSS observations even if the slip occurs by one cycle. A comparison with the scientific software BERNESE 5.0 confirms the good performance of the proposed method, especially when data sampled at high frequency (e.g. every 1 s or every 5 s) are available.     

GNSS multi-frequency receiver single-satellite measurement validation method

A method is presented for real-time validation of GNSS measurements of a single receiver, where data from each satellite are independently processed. A geometry-free observation model is used with a reparameterized form of the unknowns to overcome rank deficiency of the model. The ionosphere error and non-constant biases such as multipath are assumed changing relatively smoothly as a function of time. Data validation and detection of errors are based on statistical testing of the observation residuals using the detection–identification–adaptation approach. The method is applicable to any GNSS with any number of frequencies. The performance of validation method was evaluated using multi-frequency data from three GNSS (GPS, GLONASS, and Galileo) that span 3 days in a test site at Curtin University, Australia. Performance of the method in detection and identification of outliers in code observations, and detection of cycle slips in phase data were examined. Results show that the success rate vary according to precision of observations and their number as well as size of the errors. The method capability is demonstrated when processing four IOV Galileo satellites in a single-point-positioning mode and in another test by comparing its performance with Bernese software in detection of cycle slips in precise point-positioning processing using GPS data.     

一种基于加表零偏稳定性的GNSS/SINS组合导航异常探测方法

在地面车载组合导航中,全球导航卫星系统（global navigation satellite system,GNSS）的观测值容易受地面复杂环境的干扰,导致其定位结果出现异常,严重影响GNSS/捷联惯性导航系统（strap-down inertial navigation system,SINS）组合的滤波解算。从惯导系统误差特性的角度,研究了一种基于加表零偏稳定性的组合导航异常探测新方法。该方法从加表零偏解算的异常来发现GNSS位置、速度等观测值中的粗差,并采取剔除和降权的抗差方法抵御粗差影响。通过一组车载数据的分析表明,观测粗差对加表零偏解算的影响十分显著,以此为判别条件能够准确地发现观测粗差。采用该方法后,位置误差、速度误差和姿态误差的均方根分别减小了70.8%、87.9%和77.7%,显著提高了组合导航的解算精度和鲁棒性,为组合导航数据的抗差处理提供了一种新思路。     

CORS基准站周跳探测与修复方法研究

在高精度GNSS测量中,周跳的存在直接影响到整周模糊度的解算及最终定位精度。针对目前各省市连续运行基准站网多系统观测数据的获取导致周跳探测工作量增加,该文基于甘肃省卫星定位连续运行基准站网(GSCORS)双频观测值提出了一种满足普遍条件的多系统周跳探测方法。采用相位减伪距结合电离层残差法分别对GPS和BDS原始观测值进行周跳探测与修复,通过对相应载波的模拟周跳探测发现,BDS较大的卫星钟差和伪距噪声影响了数据质量,其周跳检测量时间序列波动大于GPS,对7周以上周跳探测的精度较GPS会有1周的偏差,但电离层残差法能对BDS相位减伪距法探测残留的1周小周跳进行二次探测并修复。实验最终证实该方法能够有效探测并且正确分离GPS和BDS每个频率1周以上的周跳。     

利用L1范数和中位数选取拟准观测值

粗差探测拟准检定法的核心是拟准观测的选取。提出了L₁范数和中位数相结合的方法选取拟准观测值,并设计了相应的准则。首先利用L₁范数方法得到稳健的残差,将其中残差接近于零时对应的观测值直接确定为拟准观测值,然后将余下残差形成新的残差向量,并计算其绝对值的中位数,拟准观测值即为那些余下残差绝对值小于中位数所对应的观测值。GPS网平差和GPS单点定位计算结果表明本文提出的选取拟准观测值的方法有效可行。     

历元间差分定位模型的粗差探测法探测周跳

假设前一历元的测站点为已知点,当前历元的测站点为未知点,当前历元和前一历元相同卫星的相位观测值取差作为观测值,观测值与当前历元点位坐标的函数关系式称为历元间差分定位模型.本文以历元间差分定位模型构造误差方程,进行最小二乘平差,提出应用Baarda数据探测法对差分观测值进行粗差探测,可准确确定观测值中的周跳.实际算例验证...     

多普勒探测周跳精度分析

阐述了多普勒探测周跳的原理,采用IGS跟踪站1s采样率的观测数据,利用TE-QC软件分别生成2s、3s、4s、5s、10s、15s和30s采样率的另外几组观测数据。确定了这几组数据无周跳,人为地加入不同周数的周跳,利用多普勒方法对其进行周跳探测,并对结果进行分析与讨论。探讨了多普勒方法探测周跳的效果,给出在不同采样率下的周跳探测精度。     

一种历元间差分单站单频周跳探测与修复方法

提出了一种基于历元间单差观测值的单站单频周跳探测与修复方法。通过假定前一历元为基准站,当前历元为流动站,采用相对定位处理模式获取当前历元观测值的验后单位权中误差,并基于抗差最小二乘获取每颗卫星的观测值残差,对单站单频数据进行周跳探测与修复。通过对实测数据的验证分析表明,按照本文方法可以100%探测周跳发生的历元。并且,当至少4颗卫星未发生周跳时,如发生异常卫星数小于可视卫星数的30%,则在95%以上的情况下可以有效确定异常卫星;当异常卫星过多时,本文方法确定异常卫星的成功率会有所下降。但是,对于探测出发生周跳的异常卫星,本文方法均可100%对其周跳进行修复。     

多尺度点云噪声检测的密度分析法

当前机载激光雷达数据和影像匹配得到的点云是密集点云数据的两类主要来源,但都不可避免存在着噪声点。本文提出一种新的点云去噪算法,可适用于这两类数据中所包含的噪声点的去除。算法主要包括两步:第1步利用多尺度的密度算法去除孤立噪声和小的簇状噪声;第2步利用三角网约束将第1步中误检测为噪声的点重新归为正常点。针对真实数据进行了剔噪试验,结果表明本文提出的基于密度分析的多尺度噪声检测算法对孤立噪声和簇状噪声都有较为效,且对于质量较差的影像匹配点云的检测也能有效处理。本文算法检测率达到97%以上。     

Improved precise point positioning in the presence of ionospheric scintillation

Ionospheric disturbances can be detrimental to accuracy and reliability of GNSS positioning. We focus on how ionospheric scintillation induces significant degradation to Precise Point Positioning (PPP) and how to improve the performance of PPP during ionospheric scintillation periods. We briefly describe these problems and give the physical explanation of highly correlated phenomenon of degraded PPP estimates and occurrence of ionospheric scintillation. Three possible reasons can contribute to significant accuracy degradation in the presence of ionospheric scintillation: (a) unexpected loss of lock of tracked satellites which greatly reduces the available observations and considerably weakens the geometry, (b) abnormal blunders which are not properly mitigated by positioning programs, and (c) failure of cycle slip detection algorithms due to the high rate of total electronic content. The latter two reasons are confirmed as the major causes of sudden accuracy degradation by means of a comparative analysis. To reduce their adverse effect on positioning, an improved approach based on a robust iterative Kalman filter is adopted to enhance the PPP performance. Before the data enter the filter, the differential code biases are used for GNSS data quality checking. Any satellite whose C1–P1 and P1–P2 biases exceed 10 and 30 m, respectively, will be rejected. Both the Melbourne–Wubbena and geometry-free combination are used for cycle slip detection. But the thresholds are set more flexibly when ionospheric conditions become unusual. With these steps, most of the outliers and cycle slips can be effectively detected, and a first PPP estimation can be carried out. Furthermore, an iterative PPP estimator is utilized to mitigate the remaining gross errors and cycle slips which will be reflected in the posterior residuals. Further validation tests based on extensive experiments confirm our physical explanation and the new approach. The results show that the improved approach effectively avoids a large number of ambiguity resets which would otherwise be necessary. It reduces the number of re-parameterized phase ambiguities by approximately half, without scarifying the accuracy and reliability of the PPP solution.     

Real-time cycle slip detection in triple-frequency GNSS

The modernization of the global positioning system and the advent of the European project Galileo will lead to a multifrequency global navigation satellite system (GNSS). The presence of new frequencies introduces more degrees of freedom in the GNSS data combination. We define linear combinations of GNSS observations with the aim to detect and correct cycle slips in real time. In particular, the detection is based on five geometry-free linear combinations used in three cascading steps. Most of the jumps are detected in the first step using three minimum-noise combinations of phase and code observations. The remaining jumps with very small amplitude are detected in the other two steps by means of two-tailored linear combinations of phase observations. Once the epoch of the slip has been detected, its amplitude is estimated using other linear combinations of phase observations. These combinations are defined with the aim of discriminating between the possible combinations of jump amplitudes in the three carriers. The method has been tested on simulated data and 1-second triple-frequency undifferenced GPS data coming from a friendly multipath environment. Results show that the proposed method is able to detect and repair all combinations of cycle slips in the three carriers.     

基于多普勒辅助的电离层残差探测与修复周跳改进方法

针对典型周跳探测与修复方法中的电离层残差法当同一历元多个频点发生周跳时,无法准确探测及修复周跳的问题,提出了一种用多普勒辅助电离层残差法的周跳探测与修复的改进方法。基于实测及仿真数据,证明了该方法能够有效解决电离层残差法存在的上述问题,即使某一历元多个频点同时发生周跳,也可以准确探测周跳发生的位置、频点及相应数值,精度达到1周,有效提高了电离层残差法探测与修复周跳的准确性与可靠性。     

GNSS观测值域增强PPP定位中综合改正数质量控制算法

若全球卫星导航系统(GNSS)观测值域综合改正数中存在粗差或周跳则不可避免地会影响精密单点定位(PPP)增强的定位结果. 针对综合改正数中可能存在的异常,对综合改正数质量控制方法开展研究并提出异常识别与控制算法. 该算法根据综合改正数特点,利用经过频间和二阶历元间差分后的综合改正数组合值采用中位数法进行异常识别与定位,并对使用该异常值的卫星采用模糊度重新初始化、降权或剔除方法进行控制处理,以减少异常值对结果的影响. 以平均边长为26 km的部分香港连续运行参考站(CORS)组成的参考网以及科廷大学提供的零基线数据对该方法验证,结果表明:对30 s采样间隔的数据该方法能够有效探测出差分组合值中大部分1周以上的较大异常和部分1周以内的异常,有效控制部分异常值对定位结果的影响.      

Outlier detection algorithms and their performance in GOCE gravity field processing

The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data.     

基于电离层电子含量变化率的周跳探测方法

推导了利用GNSS双频数据的基于电离层电子含量(TEC)变化率的周跳探测方法,分析了电离层平静期和活跃期的TEC变化率,并认为对1s采样率的数据来说无论是否发生电磁暴都不影响TEC变化率法的使用,通过实验证明TEC变化率法能对1s采样率数据中的、其它周跳探测方法较难探测的小周跳进行探测,而联合利用TEC变化率法和MW法可对各自不敏感的周跳组合进行有效探测,实验证明:联合法对电离层平静期和活跃期中、采样间隔为1s的数据中的周跳都具有很高的探测成功率。     

A double-differenced cycle slip detection and repair method for GNSS CORS network

The difficulty to detect and repair cycle slip of carrier phase measurements is a key limit for continuously high accuracy of GNSS positioning and navigation services. We propose an automated cycle slip detection and repair method for data preprocessing of a CORS network. The method jointly uses double-differenced (DD) geometry-free (GF) combination and ionospheric-free observation corrected for the computed geometrical distance (IF-OMC) to estimate the cycle slips in dual-frequency observations. The DD GF combination, which is only affected by the ionospheric residual, can be used to detect cycle slips with high reliability except for special pairs such as (77, 60) on GPS L1/L2 frequencies. The detection principle of the IF-OMC observable is such that there is a large discontinuity related to the previous epoch when cycle slips occur at the present epoch. The disadvantages of these two combinations can be overcome employing the proposed detection method. The cycle slip pair (77, 60) has no effect on the GF combination, while a change of 14.65 m is derived from GPS L1/L2 observations using the IF-OMC algorithm. Using pre-determined station coordinates as precise values, we found that the accuracy of the DD IF-OMC combination was 18 mm for a 200-km CORS baseline. Therefore, cycle slips in dual-frequency observations can be correctly and uniquely determined using DD GF and IF-OMC equations. The proposed method was verified by adding simulated cycle slips in observations collected from the CORS network under a quiet ionosphere and shown to be effective. Moreover, the method was assessed with observations made during intense ionospheric activity, which generated extensive cycle slips. The results show that the algorithm can detect and repair all cycle slips apart from two exceptions relating to long data gaps.