共存点目标和扩展目标轨迹PHD滤波器
The Trajectory PHD Filter for Coexisting Point and Extended Target Tracking
AI总结：
本文提出一种通用轨迹概率假说密度滤波器，用于估计共存点目标和扩展目标的轨迹。通过最小化Kullback-Leibler散度找到最佳泊松后验近似，采用高斯密度和伽马高斯逆 Wishart 密度分别处理点目标和扩展目标，并提出L-scan近似以降低计算成本。
摘要：
本文开发了一种通用轨迹概率假说密度（TPHD）滤波器，该滤波器使用通用密度处理目标生成的测量数据，并能估计共存点目标和扩展目标的轨迹。首先，我们基于寻找最佳泊松后验近似，通过最小化Kullback-Leibler散度推导了该通用TPHD滤波器，不使用概率生成函数。其次，我们采用高效的实现方法，其中高斯密度对应于点目标，伽马高斯逆 Wishart 密度用于扩展目标。L-scan近似也被提出作为简化版本，以减轻巨大的计算成本。仿真和实验结果表明，所提出的滤波器能够正确分类目标并获得准确的轨迹估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
