一种基于多模型高斯逆Wishart PHD滤波器的空间邻近目标跟踪方法

将空间邻近目标(Closely Spaced Objects,CSOs)整体建模为扩展目标(Extended Target,ET),用随机矢量和随机矩阵分别描述CSOs质心运动和扩散状态,并采用高斯逆Wishart(Gaussian inverse Wishart,GIW)概率假设密度(Probability Hypothesis Density,PHD)滤波器实现杂波和漏检条件下CSOs的稳定跟踪.修正了原GIW-PHD滤波器量测模型和形状估计的缺陷,给出新的递推表达式,并在此基础上提出一种多(形变)模型GIW-PHD滤波器,以适应CSOs分裂和融合引起的形状变化.仿真结果表明,所提算法能够有效跟踪CSOs,状态估计比原GIW-PHD更加准确,对CSOs的变化更加敏感.

A multiple-model Gaussian inverse Wishart PHD filter for closely spaced objects tracking

The Closely Spaced Objects (CSOs) unity is treated as an extended target with a random vector and a random matrix to respectively represent the kinematic states of the centroid and the extension state. Then the Gaussian inverse Wishart implementation of extended-target PHD filter is utilized to track CSOs under clutter and miss detection. New recursive expressions are given for the ill modeling of the original GIW-PHD filter. Consequentially, multiple (shape) models can be easily incorporated with the modified GIW-PHD filter to handle extension changes caused by splitting and merging of CSOs. The effectiveness of the proposed method is demonstrated by simulations. Simulation results show that the proposed method not only improves the estimation precision of the states, but also more sensitive to the extension changes of CSOs compared with the original GIW-PHD filter.