Real-time railway plane alignment discrimination based on linear discriminant analysis of Lp norm with sparsity constraint

Abstract

To improve the accuracy of detected parameters at curves by an in-use track geometry measurement (TGM) system, a real-time railway plane alignment (RPA) discrimination method based on linear discriminant analysis of an Lp norm with sparsity constraint (LDA-Lp-SC) is proposed. A standard database to identify the radius of a track curve is established, using the relevant ledger and track TGM data. Multi-dimensional feature extraction is performed on the TGM data, and the parameters of the method are optimized to improve the accuracy of the model classification. Finally, the method is embedded in a TGM system combined with the original RPA discrimination method, so that the key parameters of RPA discrimination can be automatically switched in real time according to the classification result of the track curve radius. Compared with the original method, the method in this study greatly improves the recognition accuracy of track curve with a radius in the range of 150–20,000 m, while ensuring real-time performance.