共查询到20条相似文献,搜索用时 26 毫秒
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有色噪声模型参数改进算法 总被引:4,自引:0,他引:4
介绍了常用的有色噪声最小二乘拟合法,并针对其在动态GPS有色噪声拟合中存在的问题提出了一种改进算法。计算结果表明,该算法能有效地提高模型参数估值的精度和Kalman滤波器的数值稳定性。 相似文献
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正交距离最小二乘和加权整体最小二乘是解自变量含误差拟合问题的两种独立准则。加权整体最小二乘与正交距离最小二乘不同,它不考虑测量点与拟合点之间的连线垂直于拟合对象的几何信息,不能确保测量点到拟合对象的距离的平方和为极小值。针对该问题,本文将正交几何信息作为约束条件融入加权整体最小二乘,提出一种约束方程带有误差改正数的非线性等式约束整体最小二乘平差法。首先,把加权整体最小二乘平差的函数式看作是非线性方程,连同正交几何约束方程一并线性化,得到线性的平差函数方程;然后,采用拉格朗日乘数法推导其参数估计及精度评定公式,并给出迭代计算算法;最后,以平面直线拟合为例,对本文方法和计算算法进行验证。试验结果表明:①本文方法和算法具有可行性;②与加权最小二乘和加权整体最小二乘相比,本文方法计算的测量点到拟合直线的垂直距离平方和最小;③本文方法计算的测量点到拟合直线的距离与测量点到拟合点的距离相等。 相似文献
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为提高三维坐标转换参数的求解质量,本文基于最优化算法提出了一种稳健的公共点加权坐标转换方法。以坐标转换后公共点的点位残差加权平方和最小为目标函数,利用Nelder-Mead单纯形直接搜索算法,寻找公共点坐标分量在解算坐标转换参数时的最优权重组合。以粒子加速器磁铁的准直安装为应用场景,利用模拟数据和实测数据对本文方法进行验证。结果表明:本文方法能够有效降低粗差观测值及质量不佳观测值的权重。与最小二乘、抗差估计等方法相比,本文方法解算结果的点位残差加权平方和更小,坐标转换参数质量更优。本文方法能提高三维坐标转换参数的求解质量,尤其适用于验前精度未知、观测数据质量不佳的情况。 相似文献
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非差非组合精密单点定位需要估计电离层延迟参数,采用电离层先验改正模型约束可以辅助电离层参数解算。针对先验电离层改正量与实际观测量之间权比关系难以确定的问题,本文提出一种电离层约束权因子搜索算法,采用权因子对先验电离层改正量的方差进行调整,根据验后残差加权平方和最小原则通过搜索找出较优的权因子,利用验后残差动态调整先验电离层改正量的方差从而达到改善定位结果的目的。采用8个MGEX跟踪站的GPS/BDS观测数据对该算法进行验证。静态结果表明:对比传统约束方法,采用搜索算法后平均三维定位精度由3.96 cm提高到3.40 cm,平均收敛时间由76.3 min缩短为59.9 min。 相似文献
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Robust bayesian estimation 总被引:10,自引:2,他引:10
Yang Yuanxi 《Journal of Geodesy》1991,65(3):145-150
Classical least squares Bayesian estimation consists of minimizing the sum of the squared residuals of observations and the corrections to prior estimates of parameters.Many authors have produced more robust versions of this estimation by replacing the square by something else, such as the absolute value. In this article, three robust (M-LS, LS-M and M-M) estimators for three corresponding error models are described based on the principle of maximum likelihood type estimates (M-estimates). The influence functions of the three robust Bayesian estimators are given. The algorithm implementation problems are discussed and the expressions for the posterior variance-covariance are derived. 相似文献
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有界不确定性平差模型的迭代算法 总被引:1,自引:0,他引:1
针对现有的有界不确定性平差模型算法较为复杂且没有顾及权重的问题,该文提出了一种无需奇异值分解的迭代算法及其一种加权方法。直接采用了迭代算法求解有界不确定性平差模型的min-max准则,推导出了未知参数估值,算法概念简单,易于实现,收敛速度更快。基于该文提出的迭代算法,当系数矩阵和观测向量各自均不等权时,采用了一种加权方法,并推导了其解算过程。算例结果表明:该文提出的迭代算法是可行的,并且解算效率更高;加权后的迭代算法是有效的。 相似文献
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桥梁施工控制网的质量是工程施工总体质量的基础。探讨了GPS控制网的方案设计,精度确定及外业布设流程与方法。重点研究了GPS坐标基准的选择与确定,内业数据处理。针对高程数据异常拟合模型参数估计,提出了两步抗差估计GPS高程拟合方法,有效地保证了似大地水准拟合模型的可靠性。 相似文献
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周跳探测与修复是高精度动态GPS定位的关键技术之一,直接影响模糊度在航解算的效率。针对动态相对定位中周跳探测方法“三差法”的不足,提出一种基于站际历元二次差模型进行探测与修复周跳的新方法。首先对站际历元间二次差观测值进行粗差探测,以确定发生周跳的卫星以及周跳初值;然后基于残差平方和最小原则搜索周跳备选组合并修复周跳。理论分析和实验结果均表明,当有效共视卫星多于4颗时,大多数情况下,新方法可以准确定位并修复周跳。 相似文献
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Adaptive estimation of multiple fading factors in Kalman filter for navigation applications 总被引:2,自引:1,他引:1
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. 相似文献
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针对传统单一灰色最小二乘支持向量机(GLSSVM)高程拟合方法的不足以及LSSVM模型参数选择的随机性,该文提出了一种基于PSO-GA算法优化的灰色最小二乘支持向量机高程拟合模型。模型将灰色模型与最小二乘支持向量机模型相结合,建立GLSSVM模型,并结合粒子群算法与遗传优化算法寻找GLSSVM模型的最优参数组合。为进一步验证提出模型的可靠性与有效性,通过具体工程实例,并将拟合结果分别与粒子群算法优化的最小二乘支持向量机模型(PSO-GLSSVM),遗传算法优化的最小二乘支持向量机模型(GA-GLSSVM)及单一GLSSVM模型进行对比分析,结果表明,PSO-GA-GLSSVM模型拟合精度更好,可靠性更高,为高程拟合研究提供了一种思路。 相似文献
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Ali Jafari Mohammad Mehdi Ebadzadeh Reza Safabakhsh 《Journal of the Indian Society of Remote Sensing》2017,45(3):417-429
The normal compositional model (NCM) is a well-known and powerful model in hyperspectral unmixing which represents endmembers as independent Gaussian vectors to capture endmember variability. However, the assumption of independent endmembers diminishes the model accuracy because the high degree of correlation between endmembers of a scene and identical sources of variability demonstrate that the endmembers are dependent. This paper proposes a new hyperspectral unmixing algorithm which represents endmembers using dependent Gaussian vectors to estimate abundance fractions. To overcome the higher complexity caused by dependence assumption, this algorithm introduces new independent Gaussian vectors named Base Vectors to represent different endmembers by a weighted linear combination. Also, the proposed unmixing algorithm uses maximum likelihood method to estimate weight coefficients of Base Vectors which are used to represent mixed pixel. Finally, abundance estimation can be done using the new representation for endmembers and mixed pixel. The proposed algorithm is evaluated and compared with other state-of-the-art unmixing algorithms using simulated and real hyperspectral images. Experimental results demonstrate that the proposed unmixing algorithm can unmix pixels composed of correlated endmembers in hyperspectral images in the presence of spectral variability more accurately than previous methods. 相似文献
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TAO Benzao ZHANG Chaoyu 《地球空间信息科学学报》2005,8(3):189-192
IntroductionAdjust ment process deals with the problemsfor esti mating parameters and assessing precisionbased on observations with errors . The researchon adjust ment system involves building mathmodel , determining the opti mization rule andstudying adjust ment arithmetic . Math model ofadjust ment system(also called adjust ment mod-el)is composed of functional and stochastic mod-els . The former describes the relationship be-tween observations and the expectation of pa-rameters . The latte… 相似文献
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Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given. 相似文献
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GNSS高程拟合常用的是二次曲面拟合法,该方法需要控制点位分布均匀,针对实际作业中受观测条件的影响部分控制点位数据无法获取,影响到GNSS高程测量精度问题,引入期望极大算法(EM算法),提出高斯分布下的EM算法与二次曲面拟合法相结合的组合算法模型,运用高斯分布下的EM算法的二次曲面拟合法对缺失数据的控制点进行建模分析。该组合算法可以获得缺失数据下未知参数的最佳估值,可有效提高水平面的拟合精度。将某区域的高程拟合控制点作为实验数据,结果表明,组合算法模型可以对缺失数据进行高程拟合,检核点最大误差为0.8 cm,组合模型拟合精度较高。 相似文献
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针对时间差分载波相位/捷联惯导紧组合系统在非高斯噪声环境工作时,采用高斯混合滤波遇到的混合模型参数估计问题,提出了一种变分贝叶斯学习优化的高斯混合自适应滤波算法。该算法借鉴变分学习理论,准确高效地实现了高斯混合模型参数的自适应估计,进一步精化了滤波算法中的随机模型,能够显著提高估计精度,降低计算负担,改善滤波性能。实验结果表明,相比传统滤波算法,该算法的估计精度得到了进一步改善,运算耗时仅与拓展卡尔曼滤波相当。 相似文献