This article proposes a novel information-theoretic joint probabilistic data association filter for tracking unknown number of targets. The proposed information-theoretic joint probabilistic data association algorithm is obtained by the minimization of a weighted reverse Kullback-Leibler divergence to approximate the posterior Gaussian mixture probability density function. Theoretical analysis of mean performance and error covariance performance with ideal detection probability is presented to provide insights of the proposed approach. Extensive empirical simulations are undertaken to validate the performance of the proposed multitarget tracking algorithm.

中文翻译：

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信息论联合概率数据关联滤波器


本文提出了一种新颖的信息论联合概率数据关联滤波器，用于跟踪未知数量的目标。所提出的信息论联合概率数据关联算法是通过最小化加权反向 Kullback-Leibler 散度来近似后高斯混合概率密度函数而获得的。提出了具有理想检测概率的平均性能和误差协方差性能的理论分析，以提供对所提出的方法的见解。进行了广泛的经验模拟来验证所提出的多目标跟踪算法的性能。