Abstract: To solve the problem of multi-target tracking under the condition of low detection probability of sensors,we propose a probability hypothesis density (PHD) smoother,and give the Gaussian mixture (GM) form of the smoother. The algorithm takes use of PHD forward recursion and backward smoothing,which lessens the possibility of wrong tracking of the target under the condition of low detection probability of sensors. In addition,the simulation results demonstrate that, when comparing the smoothed PHD filtering with the unsmoothed PHD filtering,the estimation accuracy of the number and condition of targets is significantly improved.