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

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.