Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference

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TUNCER B., ORGUNER U., ÖZKAN E.

IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol.70, pp.3921-3934, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 70
  • Publication Date: 2022
  • Doi Number: 10.1109/tsp.2022.3192617
  • 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.3921-3934
  • Keywords: Kinematics, Covariance matrices, Shape, Target tracking, Partitioning algorithms, Signal processing algorithms, Computational modeling, Extended target tracking, random matrix, variational Bayes, OBJECT, MODEL
  • Middle East Technical University Affiliated: Yes

Abstract

In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive filtering framework. We utilize the variational Bayes inference method to solve the association problem and to approximate the intractable true posterior. The performance of the proposed solution is demonstrated in simulations and real-data experiments. The results show that our method outperforms the state-of-the-art methods in terms of accuracy with lower computational complexity.