More than one measurement is generated by the target per scan, when the target is detected by a high resolution sensor or there are more than one measurement source on the target surface. The probability hypothesis density(PHD)  filter shows good performance to solve the problem of extended target tracking. Aiming at the measurements partitioning of multiple extended target tracking using the PHD filter, a measurement partitioning algorithm for extended target tracking based on affinity propagation(AP) clustering is proposed. Simulation results show that the proposed algorithm can reduce the computational complexity obviously, and obtain an improved performance.