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A Track to Track Association Algorithm Based on Weighted State Correlation Similarity

机译:基于加权状态相关相似度的航迹关联算法

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In multi-sensor systems, track association plays a critical role to ensure an accurate multi-target tracking. In this study, we propose a novel statistical method based on temporal state correlation similarity. In this method, a hybrid distance metric is derived from the correlation coefficients of the covariance matrix obtained from the sequential states of individual tracks and the distances between different target states. Contrary to many association algorithms that perform association in every single scan, the proposed method processes the track states as blocks in a given time period. The effectiveness of the proposed method under unbiased sensor measurements is illustrated by various three-dimensional multi-target tracking simulation scenarios where target density and the sensor noise level significantly varies.
机译:在多传感器系统中,跟踪关联起着至关重要的作用,以确保准确的多目标跟踪。在这项研究中,我们提出了一种基于时间状态相关相似度的新型统计方法。在这种方法中,混合距离度量是从协方差矩阵的相关系数中得出的,协方差矩阵的相关系数是从各个磁道的顺序状态以及不同目标状态之间的距离获得的。与在每次扫描中执行关联的许多关联算法相反,该方法在给定的时间段内将磁道状态作为块进行处理。在目标密度和传感器噪声水平显着变化的各种三维多目标跟踪模拟方案中,说明了该方法在无偏传感器测量下的有效性。

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