一种新的机动频率和方差自适应滤波算法

A New Maneuvering Frequency and Variance Adaptive Filtering Algorithm

针对工程应用中"当前"统计模型对机动频率和最大加速度经验值依赖过大,难以根据目标的加速度变化进行实时动态调整的问题,在分析机动频率物理含义及其与加速度变化关系、位移扰动增量与加速度方差的关系基础上,提出了一种高效的机动频率和加速度方差双变量自适应算法。仿真结果证明:该算法能很好地自适应目标的加速度变化,并在不同噪声水平下有效提高跟踪精度,尤其大幅度提高了对非机动或弱机动目标的跟踪精度。

For engineering applications,the ＂current＂ statistical model has the problem of depending too much on maneuvering frequency＇s and maximum acceleration＇s experience.So it is difficult for the ＂current＂ model adjusting according to the changes of acceleration of the target real-time and dynamically. By analyzing physical meaning of maneuvering frequency and by utilizing the relationship between disturbance increment of displacement and acceleration variance,an efficient maneuvering frequency and acceleration variance double variable adaptive algorithm are presented.The contrasting simulation results of CS（Adaptive Filtering Algorithm based on the standard ＂current＂ model） and MCS（Mending Adaptive filtering algorithm based on the standard ＂current＂ model） showed that the MCS can adapt to the changes of acceleration commendably,and it can ameliorate tracking accuracy efficiently,also it can improve the accuracy under different levels of noise,and especially it can significantly improve the accuracy of non-maneuvering or weak maneuvering target tracking.