Abstract: In the traditional Gaussian mixture particle probability hypothesis density filter (GMP-PHDF), the prior state transition probability density is used as a sample important density function, which will lead to a particle degradation problem. The posterior estimation that is more approximate to the real posterior distribution, can be obtained by the incremental state update procedure of the recursive update Gaussian filter according to the gradient of the measurement function, where, however, the nonpositive definite covariance matrix will cause recursive interruption. Thus, the implementation idea of the square-root recursive update Gaussian filter (SR-RUGF) is analyzed, and the implementation steps of SR-RUGF based on the cubature Kalman filter (CKF) are given subsequently. On this basis, a sample important density function is constructed by using SR-RUGF, based on which a squareroot recursive update GMP-PHDF (SRRU-GMP-PHDF) is derived. Simulation results demonstrate that the proposed algorithm can assimilate the measurement information commendably and obtain estimation results with higher accuracy.