The Sequential Monte Carlo (SMC) implementation for the probability hypothesis density (PHD) filter, referred to as the SMC-PHD filter, is a good candidate for multi-target tracking (MTT) problems. It recursively propagates the weighted particle set that approximates the multi-target posterior density. In this paper, we propose an improved SMC-PHD filter for MTT called the particle-gating SMC-PHD filter. First, a robust gating based on particles propagated from a previous time period is proposed to select the observations of survival targets. Second, a sigma-nearest-gating is proposed to accurately select the observations of new targets. By employing only the observations obtained by the above algorithms to update the state estimations, the overall processing speed of the filter is significantly improved. In addition, a softening factor is suggested to lower the average number of clutters in the updater. This provides more accurate estimation compared with the basic SMC-PHD filter. Finally, the respective real-time and tracking performances of the proposed SMC-PHD filter are verified by the simulation results. A particle-gating based on a sample of particles propagated is proposed to select the observations of survival targets.The definitions of ownerless particles and claimed particles are proposed to select the observation of a re-detected target.A sigma-nearest-gating is proposed to select the observations of new targets.A softening factor is suggested to lower clutter intensity after the gating operation.