Distributed acoustic sensing through fiber-optical cables can contribute to traffic monitoring systems. Using data from a day of field testing on a 50 km long fiber-optic cable along a railroad track in Norway, we detect and track cars and trains along a segment of the fiber-optic cable where the road runs parallel to the railroad tracks. We develop a method for automatic detection of events and then use these in a Kalman filter variant known as joint probabilistic data association for object tracking and classification. Model parameters are specified using in-situ log data along with the fiber-optic signals. Running the algorithm over an entire day, we highlight results of counting cars and trains over time and their estimated velocities.

翻译：分布式光纤声学传感技术可为交通监测系统提供有效支撑。本文利用挪威某段50公里长铁路沿线光纤电缆的现场测试数据，对道路与铁路并行区段内的汽车与列车进行检测与跟踪。我们开发了事件自动检测方法，并将其应用于联合概率数据关联这一卡尔曼滤波变体，以实现目标跟踪与分类。模型参数基于现场日志数据与光纤信号共同确定。通过全天候算法运行，展示了汽车与列车数量随时间变化的统计结果及其速度估计值。