Target Tracking with Radar Sensors in Highway Divergence and Convergence Sections

As the junction between highways and other urban roads, the divergence and convergence sections cause vehicles to perform frequent and complex acceleration, deceleration and lane changes. This poses a challenge for traffic radar to accurately estimate the position of vehicles. The classical algorithm based depends too much on the prior model and cannot accurately track complex vehicle movements. This paper proposes an improved method to achieve accurate tracking of vehicle targets in the divergence and convergence sections. Firstly, based on the unscented Kalman filter framework, we derive the filtering algorithm framework after introducing model errors. Secondly, we design a gated recurrent neural network to learn the implicit target motion state information in the dataset through the memory of the recurrent neural network, and converted it into model error through the gating structure. Then the model error is compensated in the state estimation. In the simulation, the position and velocity errors of the proposed algorithm are reduced by 16.83% and 17.45% compared with the baseline filter.