共查询到20条相似文献，搜索用时 120 毫秒
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《武汉大学学报(信息科学版)》2016,(6)
由于星地时间观测受大气层和地球表面环境影响,时间观测序列容易出现粗差,原子钟性能也可能出现相应异常扰动,需要对粗差进行分析处理。对此,本文引入基于识别变量的自回归(auto-regressive,AR)模型异常值探测的Bayesian方法对星地时间同步钟差序列中的异常值进行探测,进一步基于迭代似然比检验法中的异常值描述模型,将异常值估值问题转化为简单的线性模型最小二乘估计问题,以期对钟差序列中的异常值进行修复。实验表明本文的方法能够准确的探测出异常值的位置并精确的估计出异常值的大小。 相似文献
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抗差卡尔曼滤波在GPS动态定位中的应用 总被引：2，自引：0，他引：2
基于Kalman滤波的GPS动态定位中，动态观测量及其相应的动态模型可能存在异常，若数据处理不考虑对这些异常的特别处理，则模糊度的估值及其所提供的动态信息将极不可靠，按抗差估计原理，文中构造了状态向量和观测值对模糊度的影响函数，并由此建立了动态GPS定位的抗差Kalman滤波解法，实际计算验证了该方法的实用性和可靠性。 相似文献
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张亮 《测绘与空间地理信息》2008,31(2):145-148
抗差估计具有较好的抗拒异常观测值及粗差的能力,而最小二乘配置又能较好地处理系统误差,本文结合两者的优点,利用抗差最小二乘配置对数字化地图进行几何纠正,其中对协方差函数采用抗差拟合,得到了较好的结果。实验证明在GIS数据处理的扫描数字化地图几何纠正中,抗差最小二乘配置在抗拒异常值和处理系统误差方面优于单纯的最小二乘估计和单纯的最小二乘配置方法。 相似文献
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稳健时序分析方法及其在边坡监测中的应用 总被引：1，自引：0，他引：1
本文将稳健估计方法引入时间序列建模,提出了基于稳健估计的自回归建模方法。采用某实测边坡两个监测点连续30期数据对该方法进行了验证计算与分析,结果表明当监测序列没有异常值时,稳健与常规自回归模型的预报精度相当;而当监测序列含有少量异常值时,稳健比常规自回归模型的预报精度有较明显的提高。 相似文献
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时间序列异常值探测的Bayes方法及其在电离层VTEC数据处理中的应用 总被引：2，自引：0，他引：2
基于Bayes统计推断理论,提出了自回归模型中异常值定位的Bayes方法;在正态-Gamma先验分布下,分别基于均值漂移模型和方差膨胀模型,提出了后验概率的计算方法,并运用Bayes方法估计了异常扰动;最后将该方法应用到电离层VTEC数据处理的建模中,比较模型修正前后预报的结果,验证了新方法的有效性。 相似文献
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北斗卫星导航系统(BDS)星载原子钟由于受到空间环境的影响和各种不确定因素的干扰,导致获取的卫星钟差数据中经常会出现异常扰动,从而降低了卫星钟性能分析的可靠性、破坏了钟差建模和预报的有效性、影响导航定位结果的精准度,需要对BDS卫星钟差数据中存在的异常值进行探测和处理。基于求和自回归移动平均模型建立BDS卫星钟差异常值探测的方差膨胀模型;运用似然比方法对BDS卫星钟差时间序列中的异常值进行探测;推导了Score检验统计量,运用最小二乘法对异常扰动的大小进行估计。试验结果表明,似然比方法能够准确探测BDS卫星钟差数据中异常值的位置,精确估计异常扰动的大小。 相似文献
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基于移动开窗法协方差估计和方差分量估计的自适应滤波 总被引：8，自引：1，他引：8
基于移动窗口协方差估计和方差分量估计，提出了一种新的自适应Kalman滤波技术。计算结果证实，该方法能有效地控制观测异常和载体状态扰动异常对动态系统参数估值的影响。 相似文献
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利用Bayes估计进行多波束测深异常数据探测 总被引：1，自引：1，他引：0
在海底地形变化连续、平缓的假设条件下,基于Bayes估计理论提出了多波束测深异常数据探测方法,并与选权迭代加权平均滤波法进行了分析和比较。结果证明,该方法可以解决测深异常值判断标准可靠性的问题,而且能合理、有效地探测出异常值。 相似文献
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针对传统基于空间插值和时间序列上的插值补全形变缺失数据的方法在空间点位分布稀疏、观测值连续缺失以及含有粗差的情况下插补效果不佳的问题,提出了一种基于抗差Kriged Kalman Filter的形变缺失数据插补方法。该方法是一种时空插值的算法,在空间点位分布稀疏时考虑时间上的相关性,在时间上出现连续缺失时考虑其他点位对插补点的影响,以提高插补缺失数据的精度。又将抗差估计融合到Kriged Kalman Filter中以抵抗形变数据中粗差对插补精度的影响。利用模拟数据及天津GPS地面沉降数据进行了实验分析。结果表明:由于该法考虑了监测点的时空相关性以及具有抗差性能,使得插补结果在空间点位稀疏、连续缺失或具有粗差的情况下都具有较高的插补精度。 相似文献
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Phase unwrapping is a key problem not only in all quantitative applications of synthetic aperture radar (SAR) interferometry but also in other fields. In this letter, a new phase unwrapping approach is investigated. Our study is based on the model of the optimum data vector. In order to autocoregister the SAR images, the proposed method takes advantage of the multibaseline optimal weighted joint data vector by extracting all the coherence information available in the neighboring pixels. Moreover, the method employs the projection of the joint signal subspace onto the corresponding noise subspace to estimate the unwrapped interferometric phases (or the terrain heights). The proposed method can accurately determine the dimensions of the noise subspace and provide the robust unwrapped interferometric phases even in the presence of the large image coregistration errors. Moreover, the multibaseline processing idea is a combination of data optimization, image coregistration, interferogram filtering, and phase unwrapping. 相似文献
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为满足当前利用车载DGPS创建或更新道路信息的实际需求,研制了道路线形参数的计算方法。在此,鉴于车辆行驶线与道路中线之间存在着偏离,从理论上导出了不同道路线形下GPS数据的误差方程式以及连接各种线形的条件方程式。针对误差方程式中未知参数互不独立的情况,采取联合直线段与曲线段一并平差解算法。据此即可求出各段的线形参数并进行精度评定,最后通过算例验证了此方法的有效性与实用性。 相似文献
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部分变量误差模型(partial EIV model)的加权整体最小二乘(weighted total least-squares,WTLS)估计不具备抵御粗差的能力。鉴于粗差可能同时出现在观测值和系数矩阵中,本文在提出部分变量误差模型WTLS估计的两步迭代解法的基础上,运用抗差M估计的等价权方法,发展了一种整体抗差最小二乘(TRLS)估计方法,并采用一致最大功效统计量确定降权因子。针对WTLS估计两步迭代解法的特点,设计了两个不同的降权方案:第1个方案是在估计系数矩阵元素时,不对观测值降权,仅对系数矩阵降权;第2个方案是在估计系数矩阵元素时,既对系数矩阵降权,同时也对观测值降权。通过对模拟2D仿射变换和线性拟合实例进行计算和分析,结果表明第1方案优于第2方案,并且优于基于残差和验后单位权方差的抗差估计和现有的变量误差模型抗差估计。 相似文献
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Sign-constrained robust least squares, subjective breakdown point and the effect of weights of observations on robustness 总被引：3，自引：1，他引：3
Peiliang Xu 《Journal of Geodesy》2005,79(1-3):146-159
The findings of this paper are summarized as follows: (1) We propose a sign-constrained robust estimation method, which can
tolerate 50% of data contamination and meanwhile achieve high, least-squares-comparable efficiency. Since the objective function
is identical with least squares, the method may also be called sign-constrained robust least squares. An iterative version
of the method has been implemented and shown to be capable of resisting against more than 50% of contamination. As a by-product,
a robust estimate of scale parameter can also be obtained. Unlike the least median of squares method and repeated medians,
which use a least possible number of data to derive the solution, the sign-constrained robust least squares method attempts
to employ a maximum possible number of good data to derive the robust solution, and thus will not be affected by partial near
multi-collinearity among part of the data or if some of the data are clustered together; (2) although M-estimates have been
reported to have a breakdown point of 1/(t+1), we have shown that the weights of observations can readily deteriorate such results and bring the breakdown point of
M-estimates of Huber’s type to zero. The same zero breakdown point of the L
1-norm method is also derived, again due to the weights of observations; (3) by assuming a prior distribution for the signs
of outliers, we have developed the concept of subjective breakdown point, which may be thought of as an extension of stochastic
breakdown by Donoho and Huber but can be important in explaining real-life problems in Earth Sciences and image reconstruction;
and finally, (4) We have shown that the least median of squares method can still break down with a single outlier, even if
no highly concentrated good data nor highly concentrated outliers exist.
An erratum to this article is available at . 相似文献
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Chuanfa Chen Yifu Wang Yanyan Li Tianxiang Yue 《Journal of the Indian Society of Remote Sensing》2018,46(4):491-499
Due to the penetration ability of airborne light detection and ranging (lidar) into tree crowns, data pits commonly appear in lidar-derived canopy height models (CHMs). They have a seriously negative effect on the quality of tree detection and subsequent biophysical measurements. To construct a pit-free CHM, an algorithm based on robust locally weighted regression and robust z-score was presented to remove data pits. The significant advantage of the new algorithm is parameter-free, which makes it efficient and robust for practical applications. A numerical test and a real-world example were respectively employed to assess the performance of our method for CHM construction, and its results were compared with those of three classical methods including natural neighbor interpolation of the highest point method, mean and median filters. The numerical test demonstrates that our algorithm is more accurate than the other methods for generating pit-free CHMs under the presence of data pits. The real-world example shows that compared with the classical methods, our method has a better ability of data pit removal. Moreover, our method performs better than the other methods for deriving plot-level maximum tree height from CHMs. In a word, the new method shows high potential for pit-free CHM construction. 相似文献
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冉典 《测绘与空间地理信息》2014,(8):84-86
针对传统灰色模型建模过程中易受观测数据随机噪声干扰的影响,利用抗差卡尔曼滤波理论能够有效地估计含有噪声的观测值的优点,构建了基于抗差卡尔曼滤波的GM(1,1)模型。结合实例,验证了该模型在一定程度上可以提高变形监测预测精度,更好地反映观测对象的变形趋势。 相似文献