Abstract: Multi-target filtering using Probability Hypothesis Density (PHD) in multi-sensor case is based on assumption model to avoid being computationally intractable. Based on describing target state space and sensor observation space by Random Finite Set (RFS) method, and on the analysis of detection probability, likelihood function and clutter distribution under the multi-sensor universal assumption model, the multi-sensor version of multi-target PHD filter is constructed by Probability Generating Functional (PGFL), the multi-sensor labeling particle Sequential Monte Carlo PHD (SMC-PHD) filtering algorithm is presented to implement this fiter with lower computational complexity. Finally, the better estimation of target number and track-valued state are obtained by simulation.