A Novel Reconstruction Method for Measurement Data Based on MTLS Algorithm

Tianqi Gu, Chenjie Hu, Dawei Tang, Tianzhi Luo

Research output: Contribution to journalArticlepeer-review


Reconstruction methods for discrete data, such as the Moving Least Squares (MLS) and Moving Total Least Squares (MTLS), have made a great many achievements with the progress of modern industrial technology. Although the MLS and MTLS have good approximation accuracy, neither of these two approaches are robust model reconstruction methods and the outliers in the data cannot be processed effectively as the construction principle results in distorted local approximation. This paper proposes an improved method that is called the Moving Total Least Trimmed Squares (MTLTS) to achieve more accurate and robust estimations. By applying the Total Least Trimmed Squares (TLTS) method to the orthogonal construction way in the proposed MTLTS, the outliers as well as the random errors of all variables that exist in the measurement data can be effectively suppressed. The results of the numerical simulation and measurement experiment show that the proposed algorithm is superior to the MTLS and MLS method from the perspective of robustness and accuracy.
Original languageEnglish
Article number6449
Pages (from-to)1-17
Number of pages17
Issue number22
Publication statusPublished - 12 Nov 2020


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