【摘要】 针对大量虚假关联鬼点导致的跟踪精度下降问题，提出了一种基于角度量测与目标运动特性融合的双级鬼点剔除与目标跟踪算法。该算法采取“先关联后估计”的协同定位策略，通过建立角度量测噪声与定位误差之间的映射关系，构建了视域栅格地图及能量积累矩阵;剖析了视域内真实目标与虚假关联鬼点间的空间几何分布特征，基于Hough变换的思想设计了一种新型剔除判据，实现了鬼点的一级粗剔除;通过研究目标定位模糊区散布特征及运动特性，利用运动参数辨识构建预测跟踪门，从运动学层面实现了鬼点的二级精剔除。实验结果表明，所提算法在目标跟踪精度方面相较于现有算法取得了显著提升，鬼点剔除率达到91.734%，有效解决了多目标跟踪中的虚假关联问题。更多还原

【Abstract】 To mitigate the degradation of tracking accuracy induced by numerous false association ghost points,a dual-level ghost point elimination and target tracking algorithm integrating angular measurement with target motion characteristics was introduced. The proposed algorithm employed a cooperative localization strategy that prioritizes association before estimation. By establishing a mapping relationship between angular measurement noise and localization error,a field-of-view grid map and an energy accumulation matrix were constructed. Through a detailed analysis of the spatial geometric distribution characteristics of real targets and false association ghost points within the field of view,a novel elimination criterion based on Hough transform theory was developed,facilitating the primary coarse elimination of ghost points. Additionally,by examining the distribution characteristics of target localization ambiguity regions and motion features,a predictive tracking gate was constructed using motion parameter identification,enabling the secondary fine elimination of ghost points at the kinematic level. Experimental results demonstrate that the proposed algorithm significantly enhances target tracking accuracy,achieving a ghost point elimination rate of 91. 734%,thereby effectively addressing the false association problem in multi-target tracking.更多还原