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Iterative update correction and multi-frame state extraction based probability hypothesis density filter

机译：基于迭代更新校正和多帧状态提取的概率假设密度滤波器

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Probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data association between measurements and tracks. The Gaussian mixture (GM) implementation of the PHD filter is a closed-form solution to the PHD filter for linear Gaussian model. However, the Gaussian mixture PHD filter suffers from filtering performance degradation problem in multi-target tracking scenarios with low probability of detection, especially when it comes to tracking nearby targets in the imperfect probability of detection conditions. Aiming at the problem, a robust Gaussian mixture PHD algorithm for tracking multiple targets is proposed. First, a novel nearby target tracking method is introduced to reallocate the possible incorrect weights of the nearby targets. Then, a novel target state estimation scheme, making full use of the multiple previous weights of the targets, is adopted to extract the estimates of the target states. Simulation experiments have demonstrated that the proposed approach can achieve better performance in terms of target states and their number than the other related algorithms when tracking multiple nearby targets in the low probability of detection scenarios. (C) 2016 Elsevier Masson SAS. All rights reserved.
机译：概率假设密度（PHD）过滤器是避免跟踪多个目标的有效方法，因为它避免了测量值与跟踪之间的显式数据关联。 PHD滤波器的高斯混合（GM）实现是线性高斯模型的PHD滤波器的封闭形式解决方案。但是，高斯混合PHD滤波器在多目标跟踪场景中具有较低的检测概率，特别是在检测条件不完善的情况下跟踪附近的目标时，存在滤波性能下降的问题。针对该问题，提出了一种鲁棒的高斯混合PHD算法跟踪多个目标。首先，一种新颖的附近目标跟踪方法被引入，以重新分配附近目标的可能不正确的权重。然后，采用一种新颖的目标状态估计方案，充分利用目标的多个先前权重，以提取目标状态的估计。仿真实验表明，在低检测场景下跟踪多个附近目标时，与其他相关算法相比，该方法在目标状态及其数量上可以达到更好的性能。 （C）2016 Elsevier Masson SAS。版权所有。

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