In order to solve the problem that the Target Birth Intensity (TBI) is unknown in multi-target tracking, a measure-driven PHD algorithm is proposed for TBI estimation.By using augmented state space method, PHD multi-target tracking is achieved in the augmented state space composed of the real-target state space and the spurious-target state space (clutter space).By constructing the newborn target measure set, the TBI is estimated with the measure-driven method, which avoids both the dependence on the prior knowledge of TBI and the interference of unknown clutter to the real-target intensity estimation.Simulation results show that the proposed algorithm is sensitive to the change of target number and can reduce the computational complexity, which improves the accuracy of tracking obviously.