Global Navigation Satellite Systems (GNSS) are widely used in intelligent transportation systems (ITS) and autonomous driving. However, in urban canyons and tunnels, multipath, blockage, and interference reduce SNR and degrade positioning accuracy. This paper proposes a high-SNR GNSS receiver architecture based on RF front-end optimization. The design integrates a low-noise amplifier (LNA), a high-linearity mixer, and an adaptive automatic gain control (AGC) mechanism within a jointly optimized RF front-end architecture, aiming to improve signal quality at the receiver input under weak-signal conditions. Experimental results show an SNR improvement of approximately 6 dB compared with a conventional receiver, along with improved positioning accuracy under both open-sky and weak-signal conditions.
Zimu Zhou, Ran Li, Zhi Li et al.· Journal of Physics, Conferen...· 0 citations
As a core interaction in the human-vehicle-road system, automated and refined detection of driving behavior has emerged as a crucial research direction in intelligent transportation systems and advanced driver assistance systems. Traditional post-event monitoring models that rely on manual or sensor-based methods are no longer able to meet the requirements of real-time and accurate risk identification. Therefore, this study proposes the You Only Look Once - Lightweight - BiFPN - ECA (YOLO-LBE) detection method. By integrating ghost convolution and GhostC2f modules to diminish computational complexity, the study employs a weighted bidirectional feature pyramid network, and further embed an ECA module to significantly enhance the precision and stability of driver behavior detection. Experimental findings demonstrate that the improved YOLOv8 model improves mAP@0.5 by 5.3%, FPS by 32.4%, Params by 34.4%, and FLOPs by 33.3% compared to YOLOv5s. This research method outperforms existing mainstream models in terms of accuracy, efficiency, and interference tolerance, providing reliable technical support for real-time driving behavior monitoring.
Guozhu Sui, Meixia Song, Haiyun Sun et al.· Information Technology and C...· 0 citations
