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

• URL: http://arxiv.org/abs/2010.08820v2
• Date: Mon, 8 Mar 2021 09:36:36 GMT
• Title: Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference
• Authors: Bark{\i}n Tuncer, Emre \"Ozkan
• Abstract summary: 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. It is not possible to find a closed-form analytical expression for the true posterior because of the absence of conjugacy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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