Particle Filter (PF) is a sophisticated model estimation technique based on simulation. Due to the natural limitations of PF, two problems, namely particle impoverishment and sample size dependency, frequently occur during the particles updating stage and these problems will limit the accuracy of the estimation results. In order to alleviate these problems, Ant Colony Optimization is incorporated into the generic PF before the updating stage. After executing the Ant Colony optimization, impoverished particle samples will be re-positioned and closer to their locally highest likelihood distribution function. Our experimental results show that the proposed algorithm can realize better tracking performance when comparing to the generic PF, the Extended Kalman Filter and other enhanced versions of PF.