Abstract: Kamen et al. have developed the symmetric measurement equation (SME) filter as an alternative to multi-target trackers based on data association. This paper presents an improved multi-dimensional SME tracking algorithm which agrees with Kamen's for one-dimensional scenarios and avoids the ghost target problem in higher dimensions. In addition, we provide a more efficient method for computing the noise covariance matrix of the SME coefficients. This was the major computational bottleneck of earlier SME implementation, and we have reduced its complexity from at least 2$+N/2$/ operations to at most D$+4$/N$+5$/, where N is the number of targets and D the number of dimensions. Computer simulations illustrate a failure mode that the new algorithm avoids, and gives a sample comparison to a standard data-association algorithm, global nearest neighbor.!10
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机译：摘要：Kamen等。已经开发了对称测量方程（SME）过滤器，以替代基于数据关联的多目标跟踪器。本文提出了一种改进的多维SME跟踪算法，该算法与一维场景的Kamen方法一致，避免了高维中的幻影目标问题。此外，我们提供了一种更有效的方法来计算SME系数的噪声协方差矩阵。这是较早实施SME的主要计算瓶颈，我们已将其复杂性从至少2 $ + N / 2 $ /个操作降低到最多D $ + 4 $ / N $ + 5 $ /个，其中N是数字目标数和D的维数。计算机仿真说明了新算法可避免的故障模式，并与标准数据关联算法“全局最近邻居”进行了样本比较！10
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