Trajectory Tracking Method for Vehicles on Highways Utilizing Distributed Optical Fiber Sensing

This paper proposes an innovative vehicle tracking framework for highway monitoring systems utilizing Distributed Acoustic Sensing (DAS). The methodology leverages spatiotemporal positioning data obtained from DAS detection modules to achieve robust trajectory tracking across highway scenarios. Our three-phase tracking architecture consists of trajectory initialization, maintenance, and termination phases. Specifically, during the initialization phase, preliminary trajectories are constructed and critical parameters for Kalman Filter configuration are supplied, thereby establishing a foundation for high-precision tracking. In the maintenance phase, the Kalman Filter's data processing capabilities are employed to ensure consistent trajectory association and continuous motion prediction. The termination phase incorporates precise detection mechanisms for exiting vehicles. Empirical data processing results demonstrate that this method offers significant advantages in tracking precision and accuracy, validating its potential application value in intelligent highway systems.