Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference

2021-02-01
In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear in the state variables, and it is not possible to find a closed-form analytical expression for the true posterior because of the absence of conjugacy. We use the variational Bayes technique to perform approximate inference, where the Kullback-Leibler divergence between the true and the approximate posterior is minimized by performing fixed-point iterations. The update equations are easy to implement, and the algorithm can be used in real-time tracking applications. We illustrate the performance of the method in simulations and experiments with real data. The proposed method outperforms the state of the art methods when compared with respect to accuracy and robustness.
Citation Formats
B. Tuncer and E. Özkan, “Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference,” pp. 1–12, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/89283.