论文标题
基于随机矩阵的扩展目标跟踪以方向：新模型和推理
Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference
论文作者
论文摘要
在这项研究中，我们提出了一种新型的扩展目标跟踪算法，该算法能够将动态物体的程度表示为具有时间变化的方向角的椭球。对角阳性半明确基质的定义是为在随机矩阵框架内建模对象的范围，使对角线元素具有逆伽玛先验。所得的测量方程在状态变量中是非线性的，由于缺乏共轭性，因此无法找到真实后验的封闭形式的分析表达。我们使用各种贝叶斯技术进行近似推断，其中通过执行固定点迭代，可以最大程度地减少真实和近似后验之间的kullback-leibler差异。更新方程式易于实现，并且该算法可用于实时跟踪应用程序。我们说明了该方法在模拟和实验实验中的性能。相对于准确性和鲁棒性，该方法的表现优于最先进的方法。
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.
