Localization and tracking of a moving target has been established as a key problem in wireless sensor networks, with many algorithms being proposed in this area. In particular, time-difference of arrival (TDOA) localization is considered to be a cost-effective and accurate localization technique. However, traditional TDOA algorithms rely on a central node that produces an estimate of the target's location by gathering measurements from all other nodes in the network. In this work, we solve the problem by distributing the estimation among all agents in the network, avoiding problems posed by the centralized approach, such as single-node failure. Each agent in the network runs its own extended Kalman filter (EKF) in order to estimate the target's position, while a neighbor-based averaging procedure is proposed to facilitate the consensus of agents' estimates. This approach does not require each node to fully observe the process, i.e., some nodes in the network may have an insufficient number of neighbors to accurately estimate the target's position on their own. We show that the estimation error is bounded, with a numerical example illustrating the performance of the proposed algorithm.