Abstract
The track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detection method is proposed by using millimeter wave radar based on frequency modulated continuous wave (FMCW). Firstly, by constructing the RCS statistical feature data set of multiple objects in the track settlement measurement environment, a directed acyclic graph-support vector machine (DAG-SVM) based method is designed to solve the problem of track recognition in multi-object scenes. Then, the adaptive chirp-z-transform (ACZT) algorithm is used to estimate the distance between the radar and the track surface, which realizes automatic real-time track settlement detection. An experimental platform has been constructed to verify the effectiveness of the proposed method. The experimental results show that the accuracy of track classification and identification is at least 95%, and the accuracy of track settlement measurement exceeds 0.5 mm, which completely meets the accuracy requirements of the railway system.
Highlights
High-speed trains have extremely high requirements for the smoothness of the tracks.The track subsidence causes a decrease in flatness and makes the train unable to reach the designed speed
The statistical feature extraction and analysis method based on Radar cross section (RCS) data set is proposed, and the multi-class directed acyclic graph-support vector machine (DAG-support vector machine (SVM)) based recognition algorithm is applied to achieve accurate track recognition in a dynamic and static mixed multi-object scene
In addition to the identification based on RCS characteristic data, literature [14,15] realized the dynamic identification of pedestrians and vehicles in the frequency modulated continuous wave (FMCW) radar system through phase characteristics or amplitude characteristics binary
Summary
High-speed trains have extremely high requirements for the smoothness of the tracks. The track subsidence causes a decrease in flatness and makes the train unable to reach the designed speed. The statistical feature extraction and analysis method based on RCS data set is proposed, and the multi-class DAG-SVM based recognition algorithm is applied to achieve accurate track recognition in a dynamic and static mixed multi-object scene. The ACZT algorithm is adopted to achieve the high-precision distance estimation between the track and radar for complex multi-target scenarios under the requirement of lower bandwidth 1.5 GHz, realizing the distance estimation accuracy of 0.5 mm.
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