The approach of tracking maneuvering targets based on the “Current” Statistical (CS) model is widely used. The method needs to preset maneuvering frequency and maximum acceleration based on experience. In practice, the preset values are often not consistent with the actual moving state of targets and result in larger tracking errors. In order to tackle the problem, this paper initially deduces a self-adapting maneuvering frequency algorithm from the discrete state equation of the CS model. Then, an improved self-adapting acceleration covariance algorithm is presented. Simulation results show that, by using the self-adapting maneuvering frequency algorithm and the improved self-adapting acceleration covariance algorithm to track targets simultaneously, the ability to self-adapt to the fluctuation of the moving state will be improved. The tracking accuracy is also improved, and the convergence speed of the algorithm is quicker.