共查询到20条相似文献，搜索用时 15 毫秒
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The inequality-constrained least squares (ICLS) problem can be solved by the simplex algorithm of quadratic programming. The ICLS problem may also be reformulated as a Bayesian problem and solved by using the Bayesian principle. This paper proposes using the aggregate constraint method of non-linear programming to solve the ICLS problem by converting many inequality constraints into one equality constraint, which is a basic augmented Lagrangean algorithm for deriving the solution to equality-constrained non-linear programming problems. Since the new approach finds the active constraints, we can derive the approximate algorithm-dependent statistical properties of the solution. As a result, some conclusions about the superiority of the estimator can be approximately made. Two simulated examples are given to show how to compute the approximate statistical properties and to show that the reasonable inequality constraints can improve the results of geodetic network with an ill-conditioned normal matrix. 相似文献
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基于有效约束的附不等式约束平差的一种新算法 总被引：2，自引：2，他引：0
不等式约束是客观实际中普遍存在的一种约束,但目前大地测量数据处理领域并没有成熟、完整并被普遍接受的处理理论和方法。首先简要总结附不等式约束平差的各种方法及其存在的问题。然后对现有测量平差中附有等式约束的平差模型进行扩展,提出一种新的处理附有线性约束(包括等式和不等式约束)的平差方法。该方法在有效约束概念下,通过库恩-塔克条件来寻找有效约束条件,把不等式约束平差问题转化为我们熟知的等式约束平差问题,因此实现解向量与观测向量之间的显式表达。最后,用一个数值算例验证新方法的可行性,同时算例分析表明:用等式约束代替有效约束或集成约束进行平差计算,能得到正确的平差结果,但得不到正确的精度评定结果。 相似文献
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误差向量的方差-协方差阵是一般对称正定矩阵下的附不等式约束加权整体最小二乘平差模型,研究了其参数估计和精度评定问题。首先,将残差平方和极小化函数在整体最小二乘准则下转化为只包含模型参数的目标函数,同时将所有的不等式约束表示成一个等价的凝聚约束函数,并运用乘子罚函数策略将不等式约束加权整体最小二乘平差问题转化为相应的无约束最优化问题,并用BFGS方法求解。然后,将误差方程和约束函数线性展开,推导了最优解和观测量间的近似线性函数关系,运用方差-协方差传播律得到了最优解的近似方差。最后,用数值实例验证了方法的有效性和可行性。 相似文献
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在大地测量数据处理中,很多情况下可根据先验知识建立合理的不等式约束,能够改善平差结果,提高精度。首先简要总结了附不等式约束平差的各种方法及存在的问题。根据有效约束和库恩塔克条件,提出了解决不等式约束平差的新算法,把不等式约束平差转化为等式约束平差问题,从而得到解的显示表达。最后用一数值算例证明了该算法的可行性。 相似文献
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Differential carrier phase observations from GPS (Global Positioning System) integrated with high-rate sensor measurements,
such as those from an inertial navigation system (INS) or an inertial measurement unit (IMU), in a tightly coupled approach
can guarantee continuous and precise geo-location information by bridging short outages in GPS and providing a solution even
when less than four satellites are visible. However, to be efficient, the integration requires precise knowledge of the lever
arm, i.e. the position vector of the GPS antenna relative to the IMU. A previously determined lever arm by direct measurement
is not always available in real applications; therefore, an efficient automatic estimation method can be very useful. We propose
a new hybrid derivative-free extended Kalman filter for the estimation of the unknown lever arm in tightly coupled GPS/INS
integration. The new approach takes advantage of both the linear time propagation of the Kalman filter and the nonlinear measurement
propagation of the derivative-free extended Kalman filter. Compared to the unscented Kalman filter, which in recent years
is typically used as a superior alternative to the extended Kalman filter for nonlinear estimation, the virtue of the new
Kalman filter is equal estimation accuracy at a significantly reduced computational burden. The performance of the new lever
arm estimation method is assessed with simulated and real data. Simulations show that the proposed technique can estimate
the unknown lever arm correctly provided that maneuvers with attitude changes are performed during initialization. Field test
results confirm the effectiveness of the new method. 相似文献
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Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation. 相似文献
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A New Data Processing Strategy for Huge GNSS Global Networks 总被引：2，自引：4，他引：2
In Global Positioning System (GPS) data analyses, large networks are usually divided into sub-networks to solve the conflict between increasing amounts of data and limited computer resources, although an integrated analysis would provide better results. This conflict becomes even more critical with the increasing number of stations, and low-Earth-orbiting satellites and the Galileo system coming into operation. The major reason is that a huge number of ambiguity parameters are kept in the normal equation for sequential integer ambiguity fixing. In this paper, the problem is solved by a special procedure of parameter elimination for both real-valued and ambiguity-fixed solutions, based on an adapted ambiguity-fixing approach where the covariance-matrix of ambiguity parameters is not required anymore. It is demonstrated that, with the new strategy, the required memory can be reduced to one-tenth and the computation time to at least one-third compared to the existing methods, and huge GPS networks with several hundred stations can be processed efficiently on a personal computer. 相似文献
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首先，用贝叶斯（Bayes）统计理论的观点，把未知参数看作随机变量，引入未知参数的无信息先验分布函数，从数学上推导了均方误差最小意义下的正则化矩阵；然后，结合最优正则化矩阵和快速截断奇异值算法，提出了一种新的正则化方法；最后，探讨了新方法在全球卫星导航系统（Global Navigation Satellite System，GNSS）模糊度解算中的应用。通过一组GNSS模糊度解算实验，比较了最小二乘（least squares，LS）方法、L曲线岭估计和新方法的性能。结果表明，新方法解算成功率略高于L曲线岭估计，远高于LS方法；计算耗时略大于LS方法，远小于L曲线岭估计。 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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GPS网平差的多元响应-非线性最小二乘模型及其解的最优化混合算法 总被引：1，自引：1，他引：0
在控制网中,用GPS测量,可以同时测得一个点的基线向量,在具有起算数据时可获得观测点的三维坐标等多个响应变量值,将这些响应变量包含的信息综合起来,可以得到参数的更精确的估计。本文针对GPS测量数据为多元响应数据的特点,建立了一个GPS网平差的多元响应-非线性最小二乘模型,针对该模型,结合拟牛顿法和信赖域算法建立了一个新非线性优化的混合算法,该算法具有全局收敛性和超线性收敛性。 相似文献
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BRDF模型参数分阶段鲁棒性反演方法 总被引：2，自引：0，他引：2
遥感BRDF物理模型均建立于一定的假设或基于某些理想状况，其模拟的数据与观测数据之间多少会存在一些差异（误差）。利用BRDF模型反演地表参数时，如果不加选择地使用所有观测数据，势必会影响模型参数反演的准确度。遥感反演时一般都采用代价函数进行参数拟合。经典的最小二乘（LS）拟合代价函数对正态分布误差具有一定的抗干扰性，但是当观测数据含有异常值时却会导致反演结果的不稳定。最小中值平方（LMS）方法具有鲁棒性特点，反演时若将其作为代价函数，则可以有效地检测出观测数据中含有的异常值，从而可以使模型反演准确度提高。本文以遥感BRDF物理模型——SAIL模型为例，使用模拟数据与真实地面观测数据，构建LMS与LS两种代价函数，分阶段地进行地表参数的反演方法研究。结果显示，针对具有一定误差或模型不能完全表示的观测数据，本文采用的分阶段方法可以对模型参数鲁棒地反演。 相似文献
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用于GPS姿态确定的矢量化算法可等价于两级最优问题。第一级把GPS载波相位观测量转换为矢量观测量。第二级是Wahba问题,即从矢量观测量获得最佳姿态解。Wahba问题可用四元数法求解,如QUEST方法。本文采用基于小角度的迭代法求解Wahba问题。在均衡星座或均衡基线务件下,两级最优解亦是全局最优解。实验结果表明迭代解的精度与QUEST解相同。实验中也应用了改进的TRIAD算法以比较两级最优解。 相似文献
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根据总体最小二乘准则,可以将附有不等式约束的变量误差（errors-in-variables,EIV）模型转化为标准最优化问题,并运用有效集法、序列二次规划法等优化方法求解。已有算法在涉及计算目标函数的Hesse矩阵（二阶导数）时,存在计算量较大的缺陷。针对上述问题,利用基于拟牛顿法修正Hesse矩阵的序列二次规划算法解算附有不等式约束加权总体最小二乘问题,新算法减小了计算量,可以提高收敛速度。通过实例,证明了该算法具有很好的适用性和计算效率。 相似文献
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In many applications of linear model theory, homogeneous variances are assumed. In practice, however, the variances are frequently heterogeneous. Therefore, to improve the results, the unknown variances have to be estimated. The appropriateness of the estimated variances has then to be checked by a suitable statistical test procedure. Such a procedure is also useful to study models of global positioning system (GPS) carrier-phase observations. While the functional model of GPS carrier-phase observations is widely accepted, the stochastic model is still under development. As well as the neglected correlations of GPS observations, a homogenous variance function is frequently assumed. In Bischoff et al. (J Geod 78:397–404, 2005), we showed by statistical testing that the assumption of constant variances is not appropriate. In this paper, we give a procedure to estimate an individual variance function for a pair of satellites and a procedure to check the appropriateness of the estimated variances. As an example, the approach is applied to double-differenced carrier-phase GPS observations. 相似文献