基于“当前”统计模型的模糊自适应跟踪算法  

A Fuzzy Adaptive Algorithm Based on Current Statistical Model for Maneuvering Target Tracking

作　　者：王向华[1] 覃征[1] 杨慧杰[2] 杨新宇[2] 

机构地区：[1]清华大学计算机科学与技术系,北京100084 [2]西安交通大学计算机科学与技术系,陕西西安710049

出　　处：《兵工学报》2009年第8期1089-1093,共5页Acta Armamentarii

摘　　要：基本的机动目标"当前"统计模型及其自适应卡尔曼滤波算法虽能对强机动目标进行很好的跟踪,但是在跟踪弱机动目标时却存在较大的误差。针对这一问题,新算法中引入一种非线性模糊隶属度函数来自适应地调整目标加速度上下限,并从理论上分析了新算法对于弱机动目标跟踪的有效性。通过计算机仿真验证了新算法相对于基本"当前"统计模型及其自适应跟踪算法的明显优势。A basic current statistical model and an adaptive kalman filter algorithm can not track feeblish maneuvering target precisely, though it has good estimate accuracy while the target maneuvers rapidly. In order to solve this problem, a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limits of target acceleration adaptively, the validity of the new algorithm for feeblish maneuvering target was proved in theory. The simulated results show that, the new algorithm has great advantage compared with the basic current statistical model and the adaptive algorithm.

关 键 词：控制理论 机动目标跟踪 “当前”统计模型 模糊控制 仿真分析 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]