Optimal estimation and rail tracking analysis

This paper describes the application of Crosscorrelation Analysis, Time Series Modeling (Auto-Regressive Integrated Moving Average, ARIMA), and the resulting statistical linearized Kalman Filter in the alignment of railroad track measurement data. Utilizing the above mathematical processes, the alignment of multiple historic (time) sequenced measurement track runs is accomplished. The deployment of Monte Carlo simulation and the modeling of Gaussian-Markov Processes are used to develop a parameter specific threshold testing criteria, which is adaptable to varied measurement data consisting of Crosscorrelation coefficients, Energy Quality Index, and Correlation Time. In addition to the development of the fundamental processes, an MS Windows 98/XP software application program is also developed and compiled for immediate applications. This software application is compatible with commercially available Track Geometry Measurements Systems currently used by the Federal Railroad Administration (FRA) and major railway providers.