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首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Probability hypothesis density filter with imperfect detection probability for multi-target tracking
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Probability hypothesis density filter with imperfect detection probability for multi-target tracking

机译：具有不完善检测概率的多目标跟踪概率假设密度滤波器

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摘要

Probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data association between measurements and targets. However, the PHD filter cannot be directly applied to track targets in imperfect detection probability conditions. Otherwise, the performance of almost all the PHD-based filters significantly decreases. Aiming at improving the estimate accuracy as for target states and their number, a multi-target tracking algorithm using the probability hypothesis density filter is proposed,. where a novel multi-frame scheme is introduced to cope with estimates of undetected targets caused by the imperfect detection probability. According to the weights of targets at different time steps, both the previous weight array and state extraction identifier of individual targets are constructed. When the targets are undetected at some times, the states of the undetected targets are extracted based on previous weight arrays and state extraction identifiers of correlative targets. Simulation results show that the proposed algorithm effectively improves the performance of the existing relevant PHD-based filters in imperfect detection of probability scenarios. (C) 2016 Elsevier GmbH. 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目标之间的显式数据关联。但是，在不完善的检测概率条件下，PHD滤波器无法直接应用于跟踪目标。否则，几乎所有基于PHD的滤波器的性能都会大大降低。为了提高目标状态及其数目的估计精度，提出了一种使用概率假设密度滤波器的多目标跟踪算法。此处介绍了一种新颖的多帧方案，以应对由不完善的检测概率引起的未检测目标的估算。根据目标在不同时间步的权重，分别构造了先前的权重数组和各个目标的状态提取标识。当某些时候未检测到目标时，将基于先前的权重数组和相关目标的状态提取标识符来提取未检测到的目标的状态。仿真结果表明，该算法有效地提高了现有相关基于PHD的滤波器在概率场景不完善检测中的性能。 （C）2016 Elsevier GmbH。版权所有。

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