自适应模糊逻辑的多模型跟踪

Multiple model tracking based on adaptive fuzzy logic

为了克服基于"当前"统计模型的交互式多模型算法难以恰当地确定当前模型的概率,以及系统参数amax和a-max在跟踪过程中不能自适应调整的缺点,提出了一种基于自适应模糊逻辑的多模型跟踪算法。介绍了基于"当前"统计模型的交互式多模型算法,给出了算法的基本步骤。在"当前"统计模型算法基础上,提出了一种基于自适应模糊逻辑的多模型跟踪改进算法,采用模糊推理给出了模型的选择概率,以提高跟踪的速度;同时,采用蚁群算法对设计参数进行优化,以提高跟踪的精度。最后,将所设计的基于"当前"统计模型的多模型改进算法用于机动目标的跟踪仿真。实验结果表明:改进的算法使得跟踪精度提高了20%左右,机动目标跟踪一次仿真时间为0.047 s,基本满足高速、高精度跟踪目标的要求。

In the Interacting Multiple Model （IMM） algorithm based on a current statistical model, the current model probability can not easily be decided, and the system parameters amax and a-max can not be adaptively adjusted. In order to overcome the above disadvantages, an IMM algorithm based on current statistical model is presented with adaptive fuzzy logic. The working principle of the algorithm is introduced, and basic steps are given. Then, a modified IMM algorithm based on current statistical model is presented with adaptive fuzzy logic. The selection probability of the modified model is given by fuzzy inference to improve the tracking speed, and the design parameters of the modified algorithm are optimized with an ant algorithm to improve the tracking precision. Finally, the modified IMM model is used to simulate maneuvering targets. The experimental results indicate that the modified algorithm can improve the tracking precision by 20%, and the simulation time of one maneuvering tracking process is 0. 047 s,which can satisfy the system requirements of fast tracking and high precision.