一种基于椭圆RHM的扩展目标Gamma高斯混合CPHD滤波器

针对杂波环境下扩展目标形状难以估计、目标跟踪精度低等问题, 提出一种自适应估计扩展目标形状的伽玛高斯混合势概率假设密度算法(GGM-CPHD). 该算法将目标的扩展形状建模为椭圆随机超曲面模型, 并将其嵌入到GGM-CPHD 滤波器中, 更新扩展目标的质心、椭圆形状和方向等信息以完成对扩展目标的跟踪. 通过杂波环境下未知数目的扩展目标仿真实验, 表明了所提出算法在质心状态和椭圆长短轴的估计精度方面要优于传统的基于随机矩阵的伽玛高斯逆韦氏CPHD滤波器.

A Gamma Gaussian-mixture CPHD filter based on ellipse random hypersurface models for extended targets

In view of the difficulty of estimating the shape of extended targets and the low accuracy in multiple extended target tracking in the clutters, a Gamma Gaussian-mixture cardinalized probability hypothesis density filter(GGM-CPHD) which can adaptively estimate the shape of the extended targets is proposed. Firstly, the extension of targets is modeled as an ellipse random hypersurface model, and then it is embedded into the CPHD filter. The extended targets are tracked by its centroid states, the major and minor axis and orientation of ellipse. Simulation for tracking an unknown number of targets in the clutter is made, which shows that the proposed algorithm outperforms the Gamma Gaussian inverse wishart CPHD filter based on the random matrix in estimation of extension’s major and minor axis, as well as centroid states.