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中文摘要：
针对传统滤波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,将高斯过程回归融入平方根无迹卡尔曼滤波算法中,提出一种滤波方法。该算法在估计过程中采用标准的平方根无迹卡尔曼滤波算法,整个估计过程分为时间更新和状态更新两部分,采用矩阵QR分解、Cholesky分解的形式直接传播和更新协方差阵的平方根;系统状态方程和观测方程由高斯回归模型分别代替分代,过程噪声的协方差和观测噪声的协方差自适应调整。将其应用于航天器人交会对接过程中,仿真仿结果满足系统导航精确度要求,校验了算法的有效性。
英文摘要：
By integrating Gaussian process regression into the square-root unscented Kalman filter,a filter algorithm is deduced to resolve the problem that classical filter algorithms are restricted by the uncertainty of system model and noise covariance.In this algorithm,estimation stage adopting standard square-root unscented Kalman filter was composed of time update and state update,and the square-root of covariance matrix was propagated and updated by QR decomposition and Cholesky factor updating.State equation and observation equation were replaced by their regression models respectively,and corresponding noise covariance was adjusted adaptively.The simulation results show that the accuracy of the algorithm meets the request of navigation,and its effectiveness is verified.
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Game-theoretic modeling of joint topology control and power scheduling for wireless heterogeneous se
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