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.

中文翻译：

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具有方向的基于随机矩阵的扩展目标跟踪：一种新模型和推理

在这项研究中，我们提出了一种新颖的扩展目标跟踪算法，该算法能够将动态物体的范围表示为具有时变取向角的椭球。定义对角正半定矩阵以在随机矩阵框架内对对象的范围进行建模，其中对角元素具有反伽马先验。所得的测量方程在状态变量中是非线性的，并且由于不存在共轭性，因此不可能找到真实后验的封闭形式的分析表达式。我们使用变分贝叶斯技术执行近似推理，其中通过执行定点迭代将真实和近似后验之间的Kullback-Leibler差异最小化。更新方程式易于实现，该算法可用于实时跟踪应用。我们用真实数据说明了该方法在模拟和实验中的性能。与准确性和鲁棒性相比，该方法的性能优于最新方法。