Iterative algorithm for weighted total least squares adjustment

In this contribution, an iterative algorithm is developed for parameter estimation in a nonlinear measurement error model y−e = (A−EA)x, which is based on the complete description of the variance–covariance matrices of the observation errors e and of the coefficient matrix errors EA without any restriction, e.g. in the case that there are correlations among observations. This paper derives the weighted total least squares solution without applying Lagrange multipliers in a straightforward manner. The algorithm is simple in the concept, easy in the implementation, and fast in the convergence. The final exact solution can be achieved through iteration. Based on the similarity between the proposed algorithm and the ordinary least squares method, the estimate for the covariance matrix of the unknown parameters can be analogously computed by using the error propagation law. The efficacy of the proposed WTLS algorithm is demonstrated by solving three WTLS problems, i.e. a linear regression model, a planar similarity transformation and two-dimensional affine transformation in the case of diagonal and fully populated covariance matrices in both start and transformed coordinate systems.