摘要
将高斯过程回归融入平方根无迹卡尔曼滤波(SRUKF)算法,本文提出了一种不确定系统模型协方差自适应调节滤波算法.该算法分为学习和估计两部分:学习阶段用高斯过程对训练数据进行学习,得到系统回归模型及噪声协方差;估计阶段由回归模型代替状态方程和观测方程,相应的噪声协方差实时自适应调整.该方法克服了传统方法容易受系统动态模型不确定性和噪声协方差不准确限制的问题,仿真结果验证了算法的有效性.
By combining the classical square root uncented Kalman filter（SRUKF） with Gaussian process regression, we derive a filter algorithm for an uncertain system model with inaccurate noise covariance. The new algorithm includes a learning stage and an estimation stage. In the first stage, Gaussian process regression is applied to learn the training data to obtain the regression model and the noise covariance of the dynamic system. In the second stage, state equations and observation equations are substituted by their regression models, respectively; the noise covariance is adaptively adjusted by using the Gaussian kernel function real-time. Thus, the problem of uncertain system model and inaccurate noise covariance in the classical filters are solved. Simulation results show the new algorithm is effective.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2010年第2期143-146,共4页
Control Theory & Applications
