A cloud model is not suitable for non-normal distribution. In order to solve the problem, a new one-dimensional backward cloud algorithm based on uniform distribution was proposed and applied to the credibility evaluation system of simulation system. Firstly, the importance of simulation credibility was expounded, and the credibility evaluation index of the evaluation results for a type of equipment concerning anti-jamming capability was established based on the actual project background. Secondly, the system was evaluated by using the evaluation method for simulation credibility based on cloud model, and the evaluation method was improved. Finally, in order to improve the evaluation method, a one-dimensional backward cloud algorithm based on uniform distribution was derived, and the experiment was designed for verifying the validity of the algorithm. The simulation experimental results show that, the average absolute error of the proposed backward cloud algorithm is less than 5% for large data, which has high applicability and provides a way of thinking for the perfection of cloud model theory. In addition, the simulation credibility evaluation results show that, the proposed method has high accuracy and contains the data information of dispersion and agglomeration, which can provides more comprehensive evaluation and the prediction of error data.

Since the multi-radar track correlation algorithm based on transitive closure fuzzy clustering has high computational complexity, a step-by-step multi-radar track correlation algorithm based on fuzzy clustering was proposed. First, based on the Euclidean distance the track correlation was judged, and the track similar matrix was simplified through fuzzy similarity calculation. Furthermore, the calculation of the iterations was decreased. Finally, the computational demanding of the proposed algorithm was certainly reduced. The simulation results show that the proposed algorithm can determine targets' tracks accurately, saves 54% of time effectively with the high accuracy.