在分析“当前”统计模型及自适应滤波技术基础上,提出了一种机动频率模糊自适应目标跟踪(FAMF)算法。通过模糊控制方法,在线调节“当前”统计模型的机动频率参数,使模型对不同的目标机动模式有更强的自适应能力。在想定初始条件下,对FAMF算法进行MonteCarlo仿真对比实验,结果表明:FAMF算法运行稳定,适应能力强,有效的提高了“当前”统计模型的跟踪性能,便于实际应用。

Based on analyzing “current” statistical model and its adaptive filter technique, a fuzzy adaptive maneuvering frequency (FAMF) algorithm was designed. Through fuzzy control, the maneuvering frequency parameter in the "current" statistical model was adjusted on-line so that the model adaptive ability to various target maneuvering patterns of this algorithm was greatly improved. At the given initial conditions, the Monte Carlo simulation experiments about strong maneuverable target using FAMF algorithm was carried out. The simulation results show that the FAMF algorithm with better adaptability advances "current" statistical model effectively and is convenient for application.