一种新的改进的“当前”统计模型的自适应滤波算法  

A New Improved “Current” Statistical Model Adaptive Filtering Algorithm

作　　者：骆荣剑[1] 李颖[1] 钱广华[1] 

机构地区：[1]中国人民解放军重庆通信学院,重庆400035

出　　处：《科学技术与工程》2014年第2期141-145,共5页Science Technology and Engineering

摘　　要：针对标准"当前"统计模型中加速度和机动频率需要预先设定的不合理,以及在跟踪非机动和弱机动目标时存在精度不高的问题,从加速度状态方程式中推导出机动频率自适应表达式;并结合已有的加速度方差自适应算法,提出了一种新的基于"当前"统计模型的自适应卡尔曼滤波算法。仿真结果表明算法的有效性和合理性。The standard the ＂current＂ statistical model exists the problem of selecting maneuvering frequency and maximum acceleration based on experience, and the problem of low accuracy in tracking non-maneuvering or weak maneuvering target, maneuvering frequency adaptive expression has derived from the acceleration equation of state, combined with the existing acceleration variance adaptive algorithm. An improved based on a new ＂current＂ statistical model adaptive Kalman filter algorithm is proposed. Simulation results show that the algorithm is effective and reasonable.

关 键 词：“当前”统计模型 机动目标跟踪 机动频率 加速度方差 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置]