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In regard to the joint adjustment problem with different types of dataset, the functional model of each type of dataset is affected by random errors, which indicates the observation vector and coefficient ma⁃ trix are not error-free. In this paper, the weight total least squares (WTLS) method is applied to joint adjustment model. An iterative WTLS method for joint adjustment model is derived, which uses the weight scaling factor to adjust the contribution of each type of dataset. In view of the determination of the weight scaling factor, more schemes are designed, which includes the minimum discrimination function method. The results show that the prior unit weight variance method and the total least squares variance component estimation (TLS-VCE) method have their limitations. When the prior information is inaccurate or the variance components are not estimable while using the TLS-VCE method, the minimum discriminate function method withΣi = 1 n1 eˉ̂1i as its discriminate function can achieve the relative effective results. [ABSTRACT FROM AUTHOR]

In this paper, a total least squares joint (TLS-J) adjustment method is proposed to the inversion of Mogi model with vertical and horizontal observational data. The proposed method considers the errors in both observation vector and coefficient matrix of the functional model of joint adjustment problem. Three forms of the minimum discriminate function methods are adopted to determine the weight scaling factor which are used to weigh the vertical and horizontal observation data. In view of the existing ill-posed problems in the joint adjustment, the L-curve method is adopted to determine the ridge parameter. Through practical examples, the total least squares joint method is systematically applied to the inversion of the Mogi model of Changbaishan Tianchi volcano. The results show that the discriminant function as the minimum can obtain the reasonable value of pressure source parameters and the relative weight ratio, which has a certain referential value to practical applications. [ABSTRACT FROM AUTHOR]

As a prior stochastic model contains inaccurate information, the weight matrices of observation and coefficient matrix are unreasonable. To address this problem, we investigate the total least squares adjustment of partial errors-in-variables(PEIV)model with a weight scaling factor that adaptively adjusts the contribution of the observation and coefficient matrix to parameter estimation. A prior unite weight variance and minimum discriminate function method are deduced, so the proposed method is valid for a structured coefficient matrix. Some conclusions are drawn from simulations of straight line fitting and coordinate transformation. When the prior unit weight variances of observation and coefficient matrix are known and accurate, the prior unit weight variance method is very effective; the minimum discriminate function method with the ??? as its discriminate function to determine weight scaling factor yielded the best performace. [ABSTRACT FROM AUTHOR]

The Total Leasl Squares (TLS) method is applied 10 joint adjustment. An algorithm for total least squares joint adjustment with a weight scaling factor is derived. The weight scaling factor is the key to deal with joint adjustment, and methods for determining the weight scaling factor are discussed. The difference norm between the estimated and true values is used to evaluate the TLS joint adjustment simulation. The influence of different noises on the weight scaling factor is also analyzed for two simulated examples. The results show that the estimated values is related to the weight scaling factor. When priori information is accurate, the prior unit weight variance method performs the best, and when priori information is inaccurate, the minimum discriminate function method with n1Σ i=1 ǀ + n2Σ j=1ǀ V2j and ǀ n1Σ i=1 ǀ V1iǀ + n2Σ j=1ǀ V2j ǀ (1 - XTX) as its discriminate function can achieve effective results. [ABSTRACT FROM AUTHOR]