Gaussian Mixture Probability Hypothesis Density Filter Based Primary Users Tracking Algorithm for Cognitive Radios

The random set based Gaussian mixture probability hypothesis density filter algorithm is a typical multi-target tracking algorithm, it can track multiple targets when the number of the targets is unknown, but this algorithm requires the knowledge of the targets’ starting positions. In many cases, the target's initial location information is not available. In this paper, a modified Gaussian mixture probability hypothesis density filter is developed for multi-target tracking problem because the traditional Gaussian mixture probability hypothesis density filter can not work well when where the targets will appear is unknown. And the proposed algorithm is applied to track the primary users in the cognitive radio systems. A double side prediction algorithm is adopted to solve this primary user tracking problem. First, the forward prediction algorithm is used to estimate the locations of the existed primary users, and then the backward prediction algorithm is used to search the new primary users. The proposed algorithm can be used when we do not know how many primary users exist, when and where they will appear. The performance of the proposed algorithm is analyzed by simulation. Simulation results show that the proposed algorithm can track the primary users even in a high false detection environment.