Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference

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.


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Track-Before-Detect (TBD) is the problem where target state estimation and detection occur simultaneously, and is a suitable method for the detection of low-SNR targets in unthresholded sensor data. In this thesis, a new Multi-Dimensional Hough Transform (MHT) technique based on Unscented Transform is proposed for the detection of dim targets in radar data. MHT is a TBD method that fuses Hough Transform results obtained on (x-t), (y-t) and (x-y) domains in order to detect a constant velocity target. The pro...
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Orguner, Umut (2012-07-01)
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Citation Formats
B. Tuncer, U. Orguner, and E. Özkan, “Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 70, pp. 3921–3934, 2022, Accessed: 00, 2022. [Online]. Available: