1)Dept. of Surveying and Geo-informatics, Tongji University, Shanghai 2000922)Key Lab. of Advanced Surveying Engineering of State Bureau of Surveying and Mapping,Shanghai 200092
Abstract Total leastsquares method is preferrable to estimate the parameters in the linear observation models when both the observaion and designed matrix are contaminated by random errors. However, the conventional total leastsquares solution cannot deal with the illposed problems, so the regularization algorithm must be used in this case. The formulas of regularized total leastsquares solution based on Tikhonov regularization criterion are derived and the variancecovariance matrices of the estimated parameters are computed by numerical method. Finally, a simulated example is investigated to verify the regularized total least squares solution by use of the Fredholm integration equation of the first kind. The result shows that the accuracy of estimates is significantly improved with this regularized algorithm.
