The iterated-corrector probability hypothesis density (IC-PHD) filter propagates the posterior intensity of each sensor at one time step to improve tracking accuracy. However, targets cannot be estimated by the IC-PHD filter, if the detection probability of the last update sensor is low. To deal with this problem, this study presents a new multi-sensor multi-target tracking method. Analysing the iterative process of this filter, it can be observed that the measurements obtained by sensors can be divided into several measurement subsets. Then, the similarity among the measurements is described by two parts, the similarity by combining the posterior intensity updated by the measurement, and the credibility of the posterior intensity. The similarity can be used to determine whether the measurements are from one target and whether the measurement selection meets the real situation. Based on the similarity among measurements, a two-way selection approach is presented to find out measurement subsets corresponding to the true targets. In this approach, measurement and measurement subset are mutually selected. Two measurements are also mutually selected. Thus only the measurement subsets corresponding to true targets have large weights. The simulation results show that the proposed method can reduce the miss-detection and false alarm effectively.

中文翻译：

---

多传感器融合，使用测量划分进行多目标跟踪

迭代校正器概率假设密度（IC-PHD）过滤器在一个时间步长传播每个传感器的后强度，以提高跟踪精度。但是，如果最后更新传感器的检测概率较低，则无法通过IC-PHD滤波器估计目标。针对这一问题，本研究提出了一种新的多传感器多目标跟踪方法。通过分析该滤波器的迭代过程，可以观察到，传感器获得的测量结果可以分为几个测量子集。然后，测量之间的相似度由两部分描述，即通过组合由测量更新的后强度和后强度的可信度来描述相似度。相似性可用于确定测量是否来自一个目标以及测量选择是否符合实际情况。基于测量之间的相似性，提出了一种双向选择方法来找出与真实目标相对应的测量子集。在这种方法中，测量和测量子集是相互选择的。两个测量值也相互选择。因此，仅对应于真实目标的测量子集具有较大的权重。仿真结果表明，该方法可以有效减少误检和误报。测量和测量子集是相互选择的。两个测量值也相互选择。因此，仅对应于真实目标的测量子集具有较大的权重。仿真结果表明，该方法可以有效减少误检和误报。测量和测量子集是相互选择的。两个测量值也相互选择。因此，仅对应于真实目标的测量子集具有较大的权重。仿真结果表明，该方法可以有效减少误检和误报。