Abstract: The finite set statistics theory (FISST) treats the collection of target states at any given time as a set-valued multi-target state, and the corresponding collection of sensor measurements is treated as a set-valued multi-target observation. Modeling set-valued states and set-valued observations as random finite sets (RFSs) allows the problem of dynamically estimating multiple targets to be cast in an optimal Bayesian filtering framework. This theoretically optimal approach to multiple targets tracking involves set integrals on the multi-target state space, which are computationally intractable. The PHD filter is the first order moment approximation of the optimal multi-target Bayesian filter, which can track an unknown and time-varying number of targets under association uncertainty. The computational load of the multi-target tracking method based on the PHD filter is much lower than the optimal multi-target Bayesian filtering methods, so it is more applicable to engineer application. However, the particle PHD filter is still computationally intensive in dense clutter environment. This paper proposes an approach to eliminate some of the clutter from the measurement set at any particular time. The proposed approach does not influence the estimate accuracy significantly, but it alleviates the computational complexity of the particle PHD filter and improves the efficiency of filtering.