A robust weighted total least squares method

In weighted total least squares, gross errors of observation data are not taken into consideration. In order to resolve this problem, a robust method of weighted total least squares with reweighting iteration is proposed, which is based on IGG weight function. Thorough experimental evaluation with a large number of simulation datasets and a set of real-life data has been carried out. The results of parameter estimations are systematically compared with weighted total least squares, and robust weighted total least squares based on Huber weight function. It is shown that: ①more reliable parameter estimations can be obtained by two robust weighted total least squares; ②more importantly, the proposed method performs better than the two others.