论文标题
用于共存点和扩展目标跟踪的轨迹博士学位过滤器
The Trajectory PHD Filter for Coexisting Point and Extended Target Tracking
论文作者
论文摘要
本文开发了通用轨迹概率假设密度（TPHD）滤波器，该过滤器使用通用密度进行目标生成的测量，并能够估算共存点和扩展目标的轨迹。首先，我们通过最小化kullback-leibler差异而无需使用概率生成函数来找到最佳的泊松后近似值，从而提供了该通用TPHD滤波器的推导。其次，我们采用了该过滤器的有效实现，在该过滤器中，高斯密度对应于点目标和伽玛式的倒数逆想密度，以扩展目标。还提出了L扫描近似值作为简化版本，以减轻巨大的计算成本。仿真和实验结果表明，所提出的过滤器能够正确对目标进行分类并获得准确的轨迹估计。
This paper develops a general trajectory probability hypothesis density (TPHD) filter, which uses a general density for target-generated measurements and is able to estimate trajectories of coexisting point and extended targets. First, we provide a derivation of this general TPHD filter based on finding the best Poisson posterior approximation by minimizing the Kullback-Leibler divergence, without using probability generating functionals. Second, we adopt an efficient implementation of this filter, where Gaussian densities correspond to point targets and Gamma Gaussian Inverse Wishart densities for extended targets. The L-scan approximation is also proposed as a simplified version to mitigate the huge computational cost. Simulation and experimental results show that the proposed filter is able to classify targets correctly and obtain accurate trajectory estimation.
