For extended object tracking, the random matrix approach is a computationally efficient framework that is capable of estimating the kinematic state, and extension of the object jointly, and thus is gaining momentum in recent years. Existing random matrix approaches have an underlying assumption that scatter centers are symmetrically distributed around the centroid. In many real scenarios, however, they are often distributed on particular portions of the object since these parts reflect more radar energy, and measurement distributions over an object are skewed. To effectively describe such a phenomenon, this paper proposes a new measurement model using a skew normal distribution. Based on the proposed model, a variational Bayesian approach is derived to recursively estimate the kinematic state, and the extension through convergent iterations. The resultant algorithm inherits the simplicity of the random matrix approach. To cope with the possible abrupt change of kinematic state, extension, and measurement distribution over an object (especially the skewness) when a target maneuvers, a multiple model approach is presented in the information theoretic interacting multiple model framework. Effectiveness of the proposed algorithms is evaluated using simulated data, and real experimental data. Results show that the proposed algorithms outperform the existing random matrix methods when measurement distributions are skewed.

中文翻译：

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使用具有偏度的随机矩阵进行扩展对象跟踪

对于扩展对象跟踪，随机矩阵方法是一种计算效率高的框架，能够联合估计运动学状态和对象的扩展，因此近年来势头强劲。现有的随机矩阵方法有一个基本假设，即散射中心围绕质心对称分布。然而，在许多实际场景中，它们通常分布在对象的特定部分，因为这些部分反射了更多的雷达能量，并且对象上的测量分布是倾斜的。为了有效地描述这种现象，本文提出了一种使用偏态正态分布的新测量模型。基于所提出的模型，导出了一种变分贝叶斯方法来递归估计运动学状态，并通过收敛迭代进行扩展。结果算法继承了随机矩阵方法的简单性。为了应对目标机动时可能发生的运动状态、扩展和测量分布（尤其是偏度）的突然变化，在信息论交互多模型框架中提出了一种多模型方法。使用模拟数据和真实实验数据评估所提出算法的有效性。结果表明，当测量分布偏斜时，所提出的算法优于现有的随机矩阵方法。在信息论交互多模型框架中提出了多模型方法。使用模拟数据和真实实验数据评估所提出算法的有效性。结果表明，当测量分布偏斜时，所提出的算法优于现有的随机矩阵方法。在信息论交互多模型框架中提出了多模型方法。使用模拟数据和真实实验数据评估所提出算法的有效性。结果表明，当测量分布偏斜时，所提出的算法优于现有的随机矩阵方法。