中文摘要：

针对现有扩展目标跟踪算法中，形状估计复杂，在考虑漏检及杂波情况下目标跟踪精度不高等问题，提出了一种基于高斯过程回归的伽玛高斯混合势概率假设密度扩展目标跟踪算法．该算法采用星凸模型对目标进行建模，在伽玛高斯混合势概率假设密度滤波器对扩展目标运动状态估计良好的基础上，利用高斯过程回归对目标形状进行估计，实现了对扩展目标的有效跟踪．实验仿真表明，所提算法能够对目标的运动状态进行高效跟踪，且在扩展形状的估计精度、计算速度等方面要优于基于星凸随机超曲面的伽玛高斯混合势概率假设密度滤波器．

英文摘要：

In view of the complexity of estimating the shape of extended targets and the low accuracy in multiple extended target tracking in the clutters and missed detections, a Gamma Gaussian-mixture cardinalized probability hypothesis density filter with Gaussian Process Regression which can adaptively estimate the shape of the extended targets is proposed. First, the extension of targets is modeled as a starconvex model, and on the basis of good estimation performance for the motion state with the Gamma Gaussian-mixture cardinalized probability hypothesis density filter, the Gaussian Process Regression is used to estimate the shape of extended targets, thus achieving the purpose of tracking the extended target. Simulation shows that the proposed algorithm outperforms the Gamma Gaussian-mixture cardinalized probability hypothesis density filter based on the star convex random hypersurface model in estimation precision and computing speed.