Random matrix based extended target tracking with orientation: A new model and inference

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Tuncer B., Özkan E.

IEEE Transactions on Signal Processing, vol.69, pp.1910-1923, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 69
  • Publication Date: 2021
  • Doi Number: 10.1109/tsp.2021.3065136
  • Journal Name: IEEE Transactions on Signal Processing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1910-1923
  • Keywords: Target tracking, Time measurement, Covariance matrices, Shape measurement, Noise measurement, Shape, Mathematical model, Target tracking, extended target tracking, orientation, variational Bayes
  • Middle East Technical University Affiliated: Yes


© 1991-2012 IEEE.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.