论文标题
Jump Markov系统模型及其高斯混合物实现的轨迹PHD滤波器
The Trajectory PHD Filter for Jump Markov System Models and Its Gaussian Mixture Implementation
论文作者
论文摘要
轨迹概率假设密度滤波器（TPHD）能够在不增加标签或标签的情况下以第一个原理产生轨迹估计。在本文中，我们提出了一个新的TPHD滤波器，称为跳跃马尔可夫系统（JMS）模型的MM-TPHD，高度动态目标在多trajectory跟踪中的多个模型之间移动开关。首先，我们将JM的概念扩展到了操纵目标的多孔径方案，并为提出的JMS模型得出了TPHD递归。然后，我们开发了MM-TPHD递归的线性高斯混合物（LGM）实现，并考虑L-SCAN计算有效的实现。最后，模拟导致操纵多条件跟踪表明该算法的性能。
The trajectory probability hypothesis density filter (TPHD) is capable of producing trajectory estimates in first principle without adding labels or tags. In this paper, we propose a new TPHD filter referred as MM-TPHD for jump Markov system (JMS) model that the highly dynamic targets movement switches between multiple models in multi-trajectory tracking. Firstly, we extend the concept of JMS to the multi-trajectory scenario of maneuvering target and derive the TPHD recursion for the proposed JMS model. Then, we develop the linear Gaussian Mixture (LGM) implementation of MM-TPHD recursion and also consider the L-scan computationally efficient implementations. Finally, simulation results in maneuvering multi-trajectory tracking demonstrate the performance of the proposed algorithm.
