Robust Estimation Method Using Successive Approximation Algorithm to Correct Errors

In measurement practice, the residuals in least squares adjustment usually show various abnormal discrete distributions, including outliers, which is not conducive to optimization of final measured values. this paper, according physical mechanism deviation, dispersion and outlier repeated observations, it can be seen that distribution are normal phenomena, weakening influence an incorrect research direction. Then, by revealing advantages functional model processing, paper puts forward error correction idea using approximate function approach actual step step, forms a new theoretical method optimize values, greatly improves quality This theory completely different from mainstream robust estimation research.