摘要
为提高目标在强机动情况下的跟踪精度,更好地实现目标跟踪,在当前统计模型和卡尔曼滤波算法的基础上提出改进的目标跟踪方法。分析了当前统计模型,归纳出在目标弱机动和强机动情况下的优点及不足。进行强机动检测,以此判断目标的机动水平;将渐消因子引入卡尔曼滤波器,减少陈旧数据的影响,充分体现当前机动状态;在算法中在线辨识各项参数,并根据机动水平自适应地调整。仿真结果表明,改进的方法在弱机动时保持了当前统计模型的跟踪性能,而在强机动时拥有更高的跟踪精度。
In order to improve tracking precision of strongly maneuvering targets and realize better target tracking, an improved method is put forward based on current statistic model and Kalman filtering algorithm. Current statistic model is analyzed, and then the advantages and shortcomings are separately concluded while targets are in weakly and strongly maneuvering state. The measuring of maneuvering status is conducted to judge the maneuvering standard, a lessening factor is introduced in Kalman filter, which weakens the effects of former data and reflects current status, and each parameter is identified on line, and adjusted adaptively in accordance with maneuvering level. The simulation indicates the improved method maintains the usual properties, and gains better tracking precision in strongly maneuvering state.
出处
《计算机工程与应用》
CSCD
2012年第35期118-122,共5页
Computer Engineering and Applications
关键词
目标跟踪
强机动
跟踪精度
检测门限
target tracking
strongly maneuvering
tracking precision
measuring threshold
