一种改进的GM-C-CPHD空间多目标跟踪算法

随着空间目标的数目急剧上升，提高空间多目标跟踪精度成为必然要求，但空间多目标跟踪存在轨道动力学模型不完善的问题。针对该问题，提出一种改进的高斯混合势概率假设密度滤波(GM-C-CPHD)算法。通过在轨道动力学模型中考虑一个不确定性模型参数，即面质比参数(AMR)，基于协方差传递面质比参数对位置、速度状态估计的影响，提高空间目标跟踪精度。仿真分析表明:相对于GM-CPHD滤波器，目标数量的跟踪和状态估计性能均有所提高，具有良好的应用前景。

An Improved GM-C-CPHD Algorithm for Spatial Multi-Target Tracking

With the rapid increase in the number of spatial targets，it is necessary to improve the accuracy of spatial multi-target tracking (MTT).However，the existing orbital dynamics model for MTT is imperfect.To solve this problem，an improved Gaussian-mixture considering cardinalized probability hypothesis density (GM-C-CPHD) algorithm is proposed.By considering an uncertain model parameter，i.e.，the area-to-mass ratio (AMR)，in the orbital dynamics model，the influence of the AMR parameter on the estimation of position and velocity vector is considered based on covariance，with which the tracking accuracy of spatial targets is improved.The simulation results demonstrate that the performance of the target number and state estimation is improved compared with the Gaussian-mixture cardinalized probability hypothesis density (GM-CPHD) filter，which shows that the proposed algorithm has a good application prospect.