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Multi-Ellipsoidal Extended Target Tracking with Variational Bayes Inference
  • Emre Özkan ,
  • Umut Orguner
middle east technical university

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Emre Özkan
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Umut Orguner
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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 accuracy with lower computational complexity.
2022Published in IEEE Transactions on Signal Processing volume 70 on pages 3921-3934. 10.1109/TSP.2022.3192617