Currently the traditional multiple extended target tracking algorithms based on random finite set(RFS) cause a large amount of computing, misclassification and low tracking performance. Therefore, this paper proposes an unknown newly born multiple extended targets tracking algorithm using mean shift iteration based on probability hypothesis density(PHD). Firstly, the state of newly born targets are obtained by associating the clustered measurements. Then, the centroids of measurement set are obtained by mean shift iteration which maps the multiple measurements to a single measurement. Finally, the proposed algorithm is performed by using the Sequential Monte Carlo method. The simulation shows that the proposed algorithm has significantly lower complexity, the higher tracking efficiency and the more robust performance than the traditional algorithm at targets crossing.