Info Rail displacement measurement in shaking table tests via a method integrating KLT feature tracker and extended Kalman filter

Abstract

Shaking table tests are widely used to evaluate seismic effects on railway structures, but accurately measuring rail displacement remains a significant challenge owing to the nonlinear characteristics of large displacements, ambient noise interference, and limitations in displacement meter installation. In this paper, a novel method that integrates the Kanade-Lucas-Tomasi (KLT) feature tracker with an extended Kalman filter (EKF) is presented for measuring rail displacement during shaking table tests. The method employs KLT feature tracker and a random sample consensus algorithm to extract and track key feature points, while EKF optimally estimates dynamic states by accounting for system noise and observation errors. Shaking table test results demonstrate that the proposed method achieves an acceleration root mean square error of 0.300 m/s² and a correlation with accelerometer data exceeding 99.7%, significantly outperforming the original KLT approach. This innovative method provides a more efficient and reliable solution for measuring rail displacement under large nonlinear vibrations.