Measurement Data Fitting Based on Moving Least Squares Method

Abstract EN

In the electromagnetic field measurement data postprocessing, this paper introduced the moving least squares (MLS) approximation method.

The MLS combines the concept of moving window and compact support weighting functions.

It can be regarded as a combination of weighted least squares and segmented least square.

The MLS not only can acquire higher precision even with low order basis functions, but also has good stability due to its local approximation scheme.

An attractive property of MLS is its flexible adjustment ability.

Therefore, the data fitting can be easily adjusted by tuning weighting function’s parameters.

Numerical examples and measurement data processing reveal its superior performance in curves fitting and surface construction.

So the MLS is a promising method for measurement data processing.