一种混合的扩展目标跟踪方法

与传统的目标跟踪不同，扩展目标跟踪（EOT）不忽略目标的轮廓特征，同时对目标的质心运动学状态和轮廓特征进行估计。基于随机矩阵的扩展目标跟踪方法用随机正定矩阵来描述目标的轮廓特征，并且建立了适合扩展目标跟踪的量测模型。为了改善目标机动时的跟踪性能，根据椭圆（体）与正定矩阵的关系，提出基于椭圆（体）拟合的扩展目标跟踪方法。进一步地，为了综合上述两类方法的优点，提出一种混合的扩展目标跟踪方法，能够根据目标机动与否在两类方法中进行选择。仿真结果表明，该混合方法的轮廓特征估计误差低于前述两类方法，质心运动学状态的估计性能也更好。

A Hybrid Approach for Extended Object Tracking

Different from the traditional object tracking technology, extended object tracking (EOT) doesn't ignore the target's physical extension. Instead, EOT simultaneously estimates both the centroid's kinematical state and the physical extension of the target. A random matrix based EOT approach characterizes the physical extension with a random symmetrical positive definite matrix, i.e. the ellipse/ellipsoid, and establishes a measurement model which is suitable for EOT. In order to improve the tracking performance when the target maneuvers, an ellipse/ellipsoid fitting based EOT approach is proposed based on the relationship between the ellipse/ellipsoid and the symmetrical positive definite matrix. Furthermore, a hybrid approach for EOT is presented to combine the advantages of the abovementioned two EOT approaches. Simulation results show that the hybrid approach can appropriately decide whether the target is maneuvering and choose a better approach. The physical extension estimation error of the hybrid approach is lower than the other approaches, and the estimation performance of the centroid's kinematical state is also better.