High-Precision Railway Fastener Detecting via Deep Learning Radar for Intelligent Train Positioning

This article introduces FC-Radio, a novel method for real-time detection of railway fasteners traversed using millimeter-wave (MMW) radar, effectively determining a train's relative position on the track. The technology can function independently of trackside equipment and seamlessly integrate with current train control systems. Integrating signal processing with deep learning, the core components of FC-Radio are the fastener reflection extractor and the deep neural network. The former uses adaptive beamforming to optimize the signal-to-clutter ratio (SCR) and uses periodic feature detection to pinpoint the spatial location of fasteners. The latter adopts a neural network architecture comprising convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTM), and attention mechanisms for meticulous fastener detection. The experimental data were collected at the National Railway Test Center of China, encompassing a diverse range of train operational scenarios. Experimental results indicate that FC-Radio exhibits capability in the real-time and accurate detection of fasteners during the high-speed operation of trains. The counting error is remarkably low, at −0.029%, which equates to a distance error of 38.1 m over a total distance of 129.6 km.