This paper presents a framework for making distributed target tracking under significant signal propagation delays between the target and the sensors. Each sensor considered makes estimation using its own measurements compensating for the involved signal propagation delay using a deterministic sampling based algorithm proposed previously. Since the individual sensor readings might not be enough to localize the target, the sensors have to share their estimates with each other at specific time instants and correct their individual estimates. This work is mainly related to how this estimate correction and fusion should be carried out. An internal covariance approximation which keeps consistency but at the same time bypasses the track correlation problem is proposed. The results are illustrated on a challenging two-sensor bearings-only tracking scenario.