On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter
On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter
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DOI：
10.1117/12.884618
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
S. Nagappa;Daniel E. Clark
中科院分区：
文献类型：
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作者：
S. Nagappa;Daniel E. Clark
本文考虑了传感器排序对迭代校正器PHD更新的影响。众所周知，改变更新的顺序会产生不同的博士学位，然而，这些通常没有显著差异。本文考虑了一个多传感器场景，使用单个低质量传感器与良好传感器相结合，其中不良传感器使用低检测概率建模。结果表明，在迭代校正器更新中首先使用传感器还是最后使用传感器，更新后的PHD质量会发生显著变化。通过对不同多传感器配置的比较，说明了迭代PHD滤波器性能的退化。当在PHD滤波器迭代形式的最终更新中使用低检测概率的传感器时，OSPA误差最大。本文还对产品型多传感器PHD滤波器的性能进行了分析。由于传感器排序的不变性，产品多传感器滤波器表现出明显更好的性能。
This paper considers the effect of sensor ordering on the iterated-corrector PHD update. It is known that changing the order of the updates results in different PHDs, however, these are usually not significantly different. This paper considers a multisensor scenario using a single poor quality sensor in combination with good sensors, where the bad sensor is modelled using a low probability of detection. It is shown that the quality of the updated PHD varies significantly depending on whether the sensor is used first or last in the iterated-corrector update. The degradation in performance of the iterated PHD filter is illustrated using a comparison of different multisensor configurations. The OSPA error is shown to be greatest when a sensor with low probability of detection is used in the final update of the iterated form of the PHD filter. The performance of the productmultisensor PHD filter is also considered. The product multisensor filter is shown to perform significantly better due to invariance to sensor ordering.