The Labeling Gaussian Mixture Hypothesis Probability Density filter (LGM-PHD) cannot get the spawn targets. Addressing this problem, an improved algorithm is presented. Firstly, the labels are applied to the Gaussian items in the GM-PHD filter to distinguish different targets, and their tracks are determined. After that, in the period of filtering, the track labels between the current step and former step are matched, associated and maintained. Finally, the spawn threshold is used to determine if there are spawn targets or not and determine the number of possible spawn targets, then the labels for Gaussian items of new targets and possible spawn targets are reallocated. The simulation results show, in the situation of existing spawn targets, the improved algorithm has better tracking performance than the LGM-PHD.