摘要
在当前统计模型卡尔曼滤波算法的基础上,结合升半正态形模糊分布函数特性,提出了一种加速度方差两段函数自适应调整方法,该方法能自适应逼近目标真实机动并进行准确跟踪。给出了最大加速度自调整方法,克服了模型对目标最大加速度的依赖。引入强跟踪滤波算法,增强了模型对突发机动自适应跟踪的能力。理论分析和仿真结果表明,该算法提高了机动模型和系统模式的匹配程度,增强了系统对强机动目标的跟踪能力,并保持对弱机动和非机动目标良好的跟踪性能,且具有运算量小、跟踪精度高、易于工程化实现等优点。
Combining the characteristic of the rise half normality fuzzy distribution function, an adaptive adjusting method of acceleration variance is presented based on Kalman filtering algorithm of current statistical model. It is composed of two sections of functions. The real maneuvering model is approached adaptively and the target is tracked accurately using this method. An adaptive adjustment means of maximum acceleration is given and the disadvantage of the model depending on maximum acceleration is overcomed. Tracking performance is enhanced for sudden maneuvering targets by introducing a strong track filter algorithm. The theoretical analysis and simulation results show that the match between maneuvering model and system mode is improved by using the algorithm. Performance for tracking strong maneuvering targets is enhanced and a good performance for tracking general motion is maintained. The algorithm is characterized by simple calculation, high tracking precision and easy realization.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第9期1937-1940,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(51179158)资助课题
关键词
当前统计模型
自适应滤波
强跟踪
机动目标
current statistical model
adaptive filtering
strong tracking
maneuvering target
