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Novel N-scan GM-PHD-based approach for multi-target tracking

机译：基于新型N扫描GM-PHD的多目标跟踪方法

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摘要

The GM-PHD-based filter has been proposed as an alternative of the PHD filter to estimate the first-order moment of the multi-target posterior density. The GM-PHD filter utilises a weighted summation of Gaussian components to estimate the target states. This filter and its recent variants perform state extraction of the targets based on the target weights. However, due to different uncertainties such as noisy observation, miss-detection, clutter or occlusion, the weight of a target is decreased and the estimation of the target is lost in some steps. In this study, the authors develop a simple and effective N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.
机译：已经提出了基于GM-PHD的滤波器作为PHD滤波器的替代方案，以估计多目标后验密度的一阶矩。 GM-PHD滤波器利用高斯分量的加权和来估计目标状态。该过滤器及其最近的变体根据目标权重执行目标的状态提取。然而，由于诸如噪声观察，漏检，杂波或遮挡之类的不同不确定性，目标的重量减小并且目标的估计在某些步骤中丢失。在这项研究中，作者开发了一种简单有效的N扫描方法，该方法利用目标的重量历史来改善基于GM-PHD的方法的性能。他们建议给每个高斯分量分配一个标签，一个权重历史和一个二进制置信度指标，并及时传播它们。然后，他们介绍了一种新颖的N扫描状态提取算法，可以根据目标状态在N个最后步骤中的历史来估计目标状态。为了研究所提出的N扫描方法的效率，将其应用于GM-PHD滤波器及其几种最新变体。针对各种不确定性提供的实验结果证明了该方法的有效性。

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