论文标题
鲁棒雷达单一对象跟踪的变分贝叶斯
Variational Bayes for robust radar single object tracking
论文作者
论文摘要
我们通过雷达来解决对象跟踪以及处理异常值的当前最新方法的鲁棒性。标准跟踪算法从雷达图像空间提取检测到在过滤阶段使用它。过滤由卡尔曼过滤器进行，该滤波器假设高斯分布式噪声。但是，此假设并不能说明大型建模错误，并且导致突然动作过程中的跟踪性能差。我们将高斯总和过滤器（多个假设跟踪器的单对象变体）作为基线，并通过与比高斯更重的分布建模过程噪声来提出修改。变分的贝叶斯提供了一种快速，计算上便宜的推理算法。我们的模拟表明，在存在过程离群值的情况下，鲁棒跟踪器在跟踪单个对象时优于高斯总和过滤器。
We address object tracking by radar and the robustness of the current state-of-the-art methods to process outliers. The standard tracking algorithms extract detections from radar image space to use it in the filtering stage. Filtering is performed by a Kalman filter, which assumes Gaussian distributed noise. However, this assumption does not account for large modeling errors and results in poor tracking performance during abrupt motions. We take the Gaussian Sum Filter (single-object variant of the Multi Hypothesis Tracker) as our baseline and propose a modification by modelling process noise with a distribution that has heavier tails than a Gaussian. Variational Bayes provides a fast, computationally cheap inference algorithm. Our simulations show that - in the presence of process outliers - the robust tracker outperforms the Gaussian Sum filter when tracking single objects.
