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Uplink Transmission Performance Evaluation and Prediction of Railway Balise based on AHP-WNN

In high-speed railways, balise uplink transmission directly affects the reliability of information exchange between trains and the ground. Hence, it is critical to evaluate and predict the transmission performance of balise. In this study, a method combining the analytic hierarchy process (AHP) and wavelet neural network (WNN) is proposed to accomplish this task using the uplink signal parameters. The AHP is used to build the comprehensive evaluation system by analyzing the dynamic parameters. The final weight vector is obtained through a weight distribution and consistency test, which can be used to calculate the comprehensive evaluation index (BTI) for transmission performance of balise. By constructing BTI time-varying sequences, a Morlet-wavelet-based WNN is developed for BTI prediction. WNN has the advantages of fast convergence and time-frequency local analysis, which can learn the historical changes to accurately predict the BTI in the future. Experiments show that this method can well fit the change laws of BTI sequences over time and accurately evaluate and predict the transmission performance of balise. This effectively guides the field to master the application status of balises and improve the operation and maintenance efficiency of railway infrastructure.