Random matrix extended target tracking for trajectory-aligned and drifting targets

In this paper, we propose two random matrix based extended target tracking models, which apply to the trajectory-aligned and drifting target motions. The trajectory-aligned model is specifically designed to handle targets moving along the direction of their extent orientations, while the drift model is tailored to targets whose trajectories deviate from their orientations in time. We utilise the well-known variational Bayes method to perform inference and obtain posterior densities via computationally efficient, analytical, iterative steps. Through comprehensive experiments conducted on simulated and real data, our methods have demonstrated superior performance compared to previous approaches in scenarios involving both drifting and trajectory-aligned targets. These results highlight the efficacy of our proposed models in accurately tracking targets and estimating their extent. We propose two random matrix based extended target tracking models, which apply to the trajectory-aligned and drifting target motions. The trajectory-aligned model is specifically designed to handle targets moving along the direction of their extent orientations, while the drift model is tailored to targets whose trajectories deviate from their orientations in time. We utilise the well-known variational Bayes method to perform inference and obtain posterior densities via computationally efficient, analytical, iterative steps. image