视野有限的传感器网络中分布式多目标跟踪

在传感器网络的多目标跟踪研究中，大多数现有的跟踪算法通常设定网络中所有节点具有相同的视野，即所有节点都能够得到目标的测量，但在实际中，节点的感测范围通常是有限的。针对这一问题，本文提出了一种能够在感测范围有限的多传感器网络中实现多目标跟踪的分布式概率假设密度滤波算法，该算法通过融合传感器网络视野范围内的后验概率假设密度粒子集来克服传感器节点感测范围的局限。仿真结果表明，提出的算法可以在感测范围有限的情况下实现多目标状态和数目的有效跟踪，同时可以在一定程度上抑制杂波，具有较好的跟踪稳定性。 

Distributed multi-target tracking in sensor network with limited field of view

In the study of multi-target tracking in sensor networks, most of the existing tracking algorithms generally assume that all nodes in the network have the same field of view, that is, all nodes can obtain the target measurement. But in practice, the sensing range of nodes is usually limited. To solve this problem, we proposes a distributed probability hypothesis density filtering algorithm that can realize multi-target tracking in sensor networks with limited sensing range. This algorithm overcomes the limitation of the sensing range of sensor nodes by fusing the particle set of posterior probability hypothesis density in the field of view of the sensor network. The simulation results show that the proposed algorithm can not only achieve effective tracking of multiple target states and numbers with limited sensing range, but also have a certain effect of clutter suppression, and have good tracking stability.