This paper proposes a novel multi-target tracking (MTT) algorithm for scenarios with arbitrary numbers of measurements per target. We propose the variational probabilistic multi-hypothesis tracking (VPMHT) algorithm based on the variational Bayesian expectation-maximisation (VBEM) algorithm to resolve the MTT problem in the classic PMHT algorithm. With the introduction of variational inference, the proposed VPMHT handles track-loss much better than the conventional probabilistic multi-hypothesis tracking (PMHT) while preserving a similar or even better tracking accuracy. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

翻译：本文提出了针对每个目标任意测量次数的假设情景的新颖多目标跟踪算法。 我们提议基于变式巴伊西亚期望最大化算法的变式概率多假设跟踪算法(VPMHT),以解决经典PMHT算法中的MTT问题。 随着引入变式推论,拟议的VPMHT处理轨道损失比常规概率多假设跟踪法(PMHT)处理得更好,同时保持类似甚至更好的跟踪准确性。 进行了广泛的数字模拟,以证明拟议算法的有效性。