A contaminated Gaussian distribution based method is robust for data rectification for its ability of taking probability distributions of random errors and gross errors into account simultaneously. But its application is limited because the precision of estimation depends on the selection of priori model parameters

which is difficult to obtain in practice. To avoid providing these parameters

a robust adaptive data rectification approach is proposed in this paper. First

a robust adaptive probability distribution model of errors is constructed. Adaptive factors

obtained from observations

are added to adjust the variances of the outlying observations. Then

Lagrange method is used to obtain the iterative algebraic solution. The correlation of measurements is also considered in this paper. Application to bilinear constraints process shows that the least square estimation based on the new approach can compensate the effect of gross errors effectively and give a robust estimation.