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Variational Measurement Update for Extended Object Tracking Using Gaussian Processes
IEEE Signal Processing Letters ( IF 3.9 ) Pub Date : 2021-02-18 , DOI: 10.1109/lsp.2021.3060316
Murat Kumru , Hilal Koksal , Emre Ozkan

We present an alternative inference framework for the Gaussian process-based extended object tracking (GPEOT) models. The method provides an approximate solution to the Bayesian filtering problem in GPEOT by relying on a new measurement update, which we derive using variational Bayes techniques. The resulting algorithm effectively computes approximate posterior densities of the kinematic and the extent states. We conduct various experiments on simulated and real data and examine the performance compared with a reference method, which employs an extended Kalman filter for inference. The proposed algorithm significantly improves the accuracy of both the kinematic and the extent estimates and proves robust against model uncertainties.

中文翻译:

使用高斯过程的扩展目标跟踪的变分度量更新

我们为基于高斯过程的扩展对象跟踪(GPEOT)模型提供了一个替代推理框架。该方法依靠新的测量更新为GPEOT中的贝叶斯滤波问题提供了近似解决方案,我们使用变分贝叶斯技术推导了该测量更新。所得算法有效地计算了运动状态和范围状态的近似后验密度。我们对模拟和真实数据进行了各种实验,并与参考方法进行了比较,该参考方法采用了扩展卡尔曼滤波器进行推理。所提出的算法大大提高了运动学和范围估计的准确性,并证明了对模型不确定性的鲁棒性。
更新日期:2021-03-23
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