Abstract: In this study the Regularized and simplified Monte Carlo-Joint Probabilistic Data Association Filter (RMC-JPDAF) is proposed and applied to the classical problem of multiple target tracking in a cluttered area. To encounter with the data association problem that arises due to unlabeled measurements in the presence of clutter, we have used the Joint Probabilistic Data Association Filter (JPDAF). The Monte Carlo methods are used in order to the fact that they have the ability to estimate any general state-space model with nonlinear and non-Gaussian functions for target dynamics and measurements likelihood. The Conventional implementation of Monte Carlo-JPDAF (MC-JPDAF) uses the resampling stage in order to reduce the variance of samples (called degeneracy problem); however this procedure itself causes another problem called sample impoverishment phenomenon, which is unavoidable and the tracking performance will decrease. So, we propose to use the regularized resampling stage instead, to counteract this shortcoming. Finally, the target dynamics model is used as the proposal distribution in MC-JPDAF, in order to decrease the computational cost while the performance of the tracking system is nearly maintained. The simulation results of the proposed system are presented and compared with those of the standard Monte Carlo implementation of FPDAF and the performance improvement of the proposed algorithm is proven.