Similarity based position calibration for multisource track inspection data

Track geometry inspection data have long endured positional errors and shifts, hindering the quality evaluation, deterioration analysis and maintenance work of railway track. Besides, vehicle response inspection data are also typically misaligned or shifted from each other, but it tends to be ignored by people. In light of this situation, a position calibration method on the basis of a simple similarity metric, i.e. correlation coefficient, is proposed to tackle the positional errors and shifts among multisource track inspection data concurrently. For the convenience of practical implementation, a scheme containing three parts is designed to fulfil the purpose and a two-step procedure is adopted by all the three parts. According to the performance test on a dataset containing five inspection runs of a high-speed railway in China, the positional error between inspection data and actual locations can be greatly diminished, and the data inspected at different times and from different sources are well aligned with each other, with a time consumption of 0.66 s on average for a 1 km segment.