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.

中文翻译：

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利用变分贝叶斯推理的多椭球扩展目标跟踪


在这项工作中，我们提出了一种新颖的扩展目标跟踪算法，它能够用多个椭圆表示一个目标或一组目标。每个椭圆均由未知的对称正定随机矩阵建模。所提出的模型需要解决两个具有挑战性的问题。首先，测量与子对象之间的数据关联问题。其次，涉及非共轭先验和似然性的推理问题需要在递归过滤框架内解决。我们利用变分贝叶斯推理方法来解决关联问题并逼近棘手的真实后验。所提出的解决方案的性能在模拟和真实数据实验中得到了证明。结果表明，我们的方法在准确性方面优于最先进的方法，并且计算复杂度较低。