标题： 细胞感知：交通检测 显示英文标题

标题： Cell Sensing: Traffic detection

摘要： 这项工作提出了一种被动感知系统，用于交通监测，使用环境长期演进（LTE）信号作为传统监控方法的非侵入性和可扩展的替代方案。 该方法采用双接收器架构，分析信道状态信息（CSI），以隔离由移动目标引起的差异多普勒频移，有效减轻硬件引起的相位失真。 该系统使用软件定义无线电（SDR）平台和srsRAN软件实现，在受控的室内测试中对高于6000 mm/min的速度展示了超过90%的检测准确率，并在户外评估中为行人和车辆提供了可靠的速度估计。 尽管在低速、方向模糊和城市环境中存在多径衰落等挑战，但结果验证了基于LTE的被动感知作为一种可行的交通监测方法，确定了未来研究的关键领域，如到达角（AoA）集成、机器学习和实时嵌入式系统开发。 显示英文摘要

摘要： This work presents a passive sensing system for traffic monitoring using ambient Long Term Evolution (LTE) signals as a non-intrusive and scalable alternative to traditional surveillance methods. The approach employs a dual-receiver architecture analyzing Channel State Information (CSI) to isolate differential Doppler shifts induced by moving targets, effectively mitigating hardware-induced phase impairments. Implemented with a Software Defined Radio (SDR) platform and srsRAN software, the system demonstrated over 90% detection accuracy for speeds above 6000 mm/min in controlled indoor tests, and provided reliable speed estimations for pedestrians and vehicles in outdoor evaluations. Despite challenges at low speeds, directional ambiguity, and multipath fading in urban settings, the results validate LTE-based passive sensing as a feasible traffic monitoring method, identifying critical areas for future research such as angle-of-arrival (AoA) integration, machine learning, and real-time embedded system development.