Abstract: 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.