共查询到20条相似文献，搜索用时 203 毫秒
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附有条件的参数平差模型的有偏估计 总被引：1，自引：0，他引：1
讨论附有条件的参数平差模型参数的有偏估计问题。提出了约束岭估计和约束主成分估计,并证明了它们的优良性质,最后给出了一个算例,验证了所得结果。 相似文献
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当线性回归模型的设计矩阵病态时,最小二乘（least square,LS）估值方差大且不稳定,已不是一种优良估计。为了减弱病态性,许多有偏估计法如岭估计、主成分估计、Liu估计等被提出。基于Liu估计,引入迭代的思想,提出了一种新的有偏估计法—迭代估计法。借助对称正定矩阵的谱分解,将迭代公式转化为便于解算的解析表达式,并证明迭代公式在修正因子d∈[-1,1]是收敛的。基于Liu估计中修正因子d的确定方法,在均方误差最小的情况下给出最优修正因子d的确定公式。最后,分别利用LS估计、岭估计、Liu估计和提出的迭代估计对两个算例进行计算并给出实验结果。在第一个算例中,对观测向量添加不同的扰动,结果表明迭代估计法具有更强的抗干扰能力;第二个算例的结果表明,迭代估计法所得结果更接近于真值,即迭代估计法在均方误差意义下优于LS估计、岭估计和Liu估计。 相似文献
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抗差主成分估计及应用 总被引：3，自引：0，他引：3
本文基于抗差估计和主成分估计理论，提出一种新的估计－抗差主成分估计，推导了参数的抗差主成分解，并结合算例进行了试算。结果表明：抗差主成分估计不仅可以解除法方程系数降的病态，而且对于抑制观测粗差的影响也有显著功效。 相似文献
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抗差Tikhonov正则化方法及其应用 总被引：4，自引：0，他引：4
提出了抗差Tikhonov正则化方法，并给出了3种常用的计算正则化参数的抗差估计方法，即抗差L-曲线法、抗差广义交叉检核法和抗差广义不符原理。计算结果表明，抗差Tikhonov正则化方法不仅能克服方程病态，而且能有效地控制观测异常影响。 相似文献
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稳健最优不变二次无偏估计 总被引：1，自引：0，他引：1
导出了稳健最优不变二次无偏估计、稳健最小范数二次无偏估计、稳健Ｈｅｌｍｅｒｔ估计，并说明了最优不变二次无偏估计、最小范数二次无偏估计以及Ｈｅｌｍｅｒｔ估计等均是稳健最优不变二次无偏估计的特例。 相似文献
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应变参数的抗差解及误差影响 总被引：1，自引：0，他引：1
应用弹性力学的应变分析理论和抗差估计原理，推导了应变参数的抗差解及其误差影响函数。实际算例表明，应变参数的抗差解可以有效地抵制粗差的异常影响，得到应变参数的可靠解，这对于利用高精度GPS复测资料研究大尺度的地壳运动和变形具有实际意义。 相似文献
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The proper identification and removal of outliers in the combination of rates of vertical displacements derived from GPS,
tide gauges/satellite altimetry, and GRACE observations is presented. Outlier detection is a necessary pre-screening procedure
in order to ensure reliable estimates of stochastic properties of the observations in the combined least-squares adjustment
(via rescaling of covariance matrices) and to ensure that the final vertical motion model is not corrupted and/or distorted
by erroneous data. Results from this study indicate that typical data snooping methods are inadequate in dealing with these
heterogeneous data sets and their stochastic properties. Using simulated vertical displacement rates, it is demonstrated that
a large variety of outliers (random scattered and adjacent, as well as jointly influential) can be dealt with if an iterative
re-weighting least-squares adjustment is combined with a robust median estimator. Moreover, robust estimators are efficient
in areas weakly constrained by the data, where even high quality observations may appear to be erroneous if their estimates
are largely influenced by outliers. Four combined models for the vertical motion in the region of the Great Lakes are presented.
The computed vertical displacements vary between − 2 mm/year (subsidence) along the southern shores and 3 mm/year (uplift)
along the northern shores. The derived models provide reliable empirical constraints and error bounds for postglacial rebound
models in the region. 相似文献
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Robust estimation of geodetic datum transformation 总被引：18，自引：1，他引：17
Y. Yang 《Journal of Geodesy》1999,73(5):268-274
The robust estimation of geodetic datum transformation is discussed. The basic principle of robust estimation is introduced.
The error influence functions of the robust estimators, together with those of least-squares estimators, are given. Particular
attention is given to the robust initial estimates of the transformation parameters, which should have a high breakdown point
in order to provide reliable residuals for the following estimation. The median method is applied to solve for robust initial
estimates of transformation parameters since it has the highest breakdown point. A smooth weight function is then used to
improve the efficiency of the parameter estimates in successive iterative computations. A numerical example is given on a
datum transformation between a global positioning system network and the corresponding geodetic network in China. The results
show that when the coordinates are contaminated by outliers, the proposed method can still give reasonable results.
Received: 25 September 1997 / Accepted: 1 March 1999 相似文献
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测量平差模型的抗差最小二乘解及影响函数 总被引：1，自引：0，他引：1
抗差Ｍ估计是使用最广泛、计算较简明的抗差估计法。基于多维Ｍ估计原理，本文建立了经典测量平差函数模型的抗差解，并推导出相应的误差影响函数；为了使抗差估计适于不同类型以及不同先验精度的各类观测值的混合平差，将使用等价权原理构造抗差最小二乘解式。 相似文献
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基于偏差矫正的一般理论提出了不适定问题的新的有偏估计。在病态条件下,Gauss-Markov模型参数的最优线性无偏估计,即LS估计是不稳健的,所得估值方差较大,严重偏离真值。因此,文中放弃了对参数估计无偏性的限制,考虑有偏估计的偏差,结合偏差矫正的正则化解法的一般理论提出了一种新的基于偏差矫正的有偏估计;结合岭估计中参数的选择方法确定了替代矩阵。最后通过GPS动态定位算例,验证了新估计的稳定性和有效性。 相似文献
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This paper aims at a comparative study of several measures to compensate for gross errors in kinematic orbit data. It starts with a simulation study on the influence of a single outlier in the orbit data on the gravity field solution. It is shown that even a single outlier can degrade the resulting gravity field solution considerably. To compensate for outliers, two different strategies are investigated: wavelet filters, which detect and eliminate gross errors, and robust estimators, which due to an iterative downweighting gradually ignore those observations that lead to large residuals. Both methods are applied in the scope of the analysis of a 2-year kinematic CHAMP (challenging minisatellite payload) orbit data set. In various real data studies, robust estimators outperform wavelet filters in terms of resolution of the derived gravity field solution. This superior performance is at the cost of computational load, as robust estimators are implemented iteratively and require the solution of large sets of linear equations several times. 相似文献
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C. Kotsakis 《Journal of Geodesy》2005,79(6-7):341-350
The objective of this paper is the comparison of various types of estimators that can be used in linear models with uniformly biased data. This particular case refers to adjustment problems where the available measurements are affected by a common, unknown and uniform offset. The classic least-squares (LS) unbiased estimators for this type of models are reviewed in detail, and some additional remarks on their properties and performance are given. Furthermore, a family of biased estimators for linear models with uniformly biased data is introduced, which has the potential to provide better performance (in terms of mean squared estimation error) than the ordinary LS unbiased solutions. A number of different regularization viewpoints that can be equivalently associated with these biased estimators are presented, along with a discussion on various selection strategies that can be employed for the choice of the regularization parameter that enters into the biased estimation algorithm. 相似文献
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This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods. 相似文献
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Robust estimation of systematic errors of satellite laser range 总被引：13，自引：0，他引：13
Methods for analyzing laser-ranging residuals to estimate station-dependent systematic errors and to eliminate outliers in
satellite laser ranges are discussed. A robust estimator based on an M-estimation principle is introduced. A practical calculation
procedure which provides a robust criterion with high breakdown point and produces robust initial residuals for following
iterative robust estimation is presented. Comparison of the results from the least-squares method with those of the robust
method shows that the results of the station systematic errors from the robust estimator are more reliable.
Received: 18 March 1997 / Accepted: 17 March 1999 相似文献
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自适应抗差联邦滤波算法 总被引：3，自引：2，他引：3
简要介绍了抗差估计理论；在顾及扰动异常的情况下，把自适应抗差Kalman滤波应用到联邦滤波上，对联邦滤波进行了改进，提出了一种自适应抗差联邦滤波算法。由计算结果可知，自适应抗差联邦滤波能较好地抑制载体观测异常和状态扰动异常对动态系统参数估值的影响，较好地提高导航解的精度。 相似文献