Abstract
•The modified correlation optimized warping with channel fusion (MFCOW) could decrease the positional errors to below 0.25 m.•The MFCOW method was accurate when facing missing data.•The proposed method could also align the first parts and ends of the datasets precisely.•The modified correlation optimized warping (MCOW) and MFCOW methods perform the alignment with reasonable computational time.
This paper proposes a modification to a well-known alignment method, correlation optimized warping (COW), to improve the efficiency of the method and reduce the positional errors in the measurements of linear assets. The modified method relaxes the restrictions of COW in aligning the start and end of datasets and decreases the computational time. Furthermore, the method takes advantage of the interdependencies between simultaneously measured channels to overcome the missing data problem. A case study on railway track geometry measurements was conducted to implement the proposed method and assess its performance in reducing the positioning inaccuracy of the measurements. The findings revealed that the modified method could decrease the positional errors of defects to below 25 cm in 94 % of the trials.