改进的卡尔曼滤波算法系统参数辨识仿真研究

研究系统参数辨识精度提高问题。辨识是从实验数据中提取有关系统信息的过程,由于存在噪声影响辨识精度,针对传统的卡尔曼滤波算法不能很好地提高跟踪精度且算法复杂的缺陷,为了解决实际系统辨识中参数噪声方差和观测噪声方差未知的等相关问题,提出了一种改进的无味卡尔曼滤波算法系统参数辨识方法,仿真结果表明,算法具有更好的泛化能力,在复杂的系统负载等情况下,也可以对系统的参数精确有效的进行辨识,验证了该算法是一种有效适用的系统参数辨识方法。

Simulation of System Parameter Identification Based on Kalman Filter

System parameter identification problem has been the focus of world problems.The traditional Kalman filter algorithm can not improve the tracking accuracy and computational complexity.In order to solve the problem of practical system identification parameters of the noise variance and observation noise variance unknown and other related issues,based on the advantages of the standard Kalman filter,an algorithm was proposed An improved system was based on the unscented Kalman filter parameter identification method.The simulation results show that the algorithm has better generalization ability,and in the circumstances of complex system loads,the parameters of the system can also be effectively identified,which shows that the algorithm is an effective method for identification of system parameters.