摘要
针对标准卡尔曼滤波器对系统的模型和噪声的统计特性依赖性强,而系统的准确数学模型难以建立的问题,结合联邦滤波和自适应估计理论,提出了一种基于联邦滤波的自适应算法。该算法通过残差的实际值与理论值的比值来确定误差方差阵的估计值,然后调节自适应卡尔曼滤波器的渐消因子,从而有效提高了联邦滤波器的适应能力。由仿真结果可知,改进的联邦滤波器能较好地利用测量信息对系统的相关参数进行自适应的调整,滤波结果具有很好稳定性和准确性。
Standard Kalman filter strongly depends on the system mode and the statistical characteristic of noise,unfortunately,an accurate mathematical model of system is difficult to set up,so a new adaptive algorithm is presented based on federated felting and the adaptive estimation.The new method can make certain the value of error covariance by calculating the rate of the real value and theoretical value of residual covariance,and then it can change fading factor,and enhance the filter＇s capability.By simulations and calculations,it is shown that the improved federated filter can effectively use observation information＇s and adaptively adjust some parameters of the system.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第1期108-110,共3页
Computer Engineering and Applications
关键词
联邦滤波
自适应卡尔曼滤波
预报残差
渐消因子
federated filtering
adaptive Kalman filtering
predicted residuals
fading factor
