A joint detection and tracking algorithm based on Random Finite Sets (RFS) theory was proposed for target detection and tracking in presence of clutters using multiple sensors. First, target states and measurements were described as RFS variables, RFS models of target motion including target birth, target survival and target death, and the multisensor measurements including miss detection and false alarm were constructed. The joint target detection and tracking problem was then modeled as a Bayesian optimal estimation to the target state RFS and the theoretically rigorous recursive formulas for the estimation were derived by using RFS theory. Finally, Sequential Monte Carlo (SMC) implementation was presented to the filter recursion. Simulation results demonstrate the effectiveness of the proposed algorithm and its significant improvement in performance over traditional association-based ones.