有界噪声条件下基于集员滤波的扩展目标跟踪方法

现有概率框架下的扩展目标跟踪方法需要已知系统量测噪声统计特性，然而在实际过程中量测噪声大多为边界已知而统计特性未知的有界噪声，其难以利用概率方法对扩展目标运动状态与形态进行计算。针对有界噪声条件下的扩展目标跟踪问题，提出一种基于集员滤波的扩展目标跟踪方法，该方法通过UBB椭球集合对量测噪声进行表示，并采用集员滤波对运动状态集合参数进行计算。在对扩展目标形态估计过程中，结合凸包计算几何理论中的Graham scan算法，求解包含目标形态最大误差的最小边界矩阵，最后利用仿射变换和偏移超曲面计算椭球Minkowski差的边界参数，从而对目标形态矩阵进行更新。仿真结果表明，在有界噪声条件下，相比于传统概率框架下的贝叶斯滤波方法，文中所提出的方法对目标运动和扩展形态的跟踪精度更高。

An Extended Object Tracking Method Based on Set Membership Filter with Unknown but Bounded Noise

The extended object tracking method under condition of now available probability framework requires the statistic information of the system measurement noise, however, most of the measurement noise is unknown but bounded in real object tracking systems, and the probability-based tracking methods are difficult to estimate the position and shape of the extended object accurately. For the above-mentioned reasons, an extended object tracking algorithm is proposed based on the set membership filter with unknown but bounded noise. The proposed algorithm expresses the unknown but bounded noise by using an enclosing ellipsoidal set and by using the set membership filter to calculate state set parameters. In the process of the estimation of the object shape, the Graham scan algorithm in convex hull computational geometry theory is used to calculate the minimum boundary matrix, including the maximum error of the object shape. To obtain the updated object shape matrix, the boundary parameters of Minkowski different are calculated by using the offset hypersurface and the affine transformation. The simulation results show that the estimation accuracy of the proposed algorithm under the UBB noise is prior to the Bayesian filters based on the traditional probability framework at the state and extent of the target.