Liu Xi, Qu Hua, Zhao Jihong, Yue Pengcheng, Wang Meng
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2016 Sep 20;16(9):1530. doi: 10.3390/s16091530.
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.
提出了一种名为最大相关熵无迹卡尔曼滤波器（MCUKF）的新算法，并将其应用于空间通信网络中的相对状态估计。众所周知，无迹卡尔曼滤波器（UKF）为解决非线性状态估计问题提供了一种有效工具。然而，UKF通常在高斯噪声中表现良好。在存在非高斯噪声的情况下，尤其是当测量受到一些重尾脉冲噪声干扰时，其性能可能会大幅下降。通过利用最大相关熵准则（MCC），所提出的算法可以增强UKF对脉冲噪声的鲁棒性。在MCUKF中，应用无迹变换（UT）来获得预测状态估计和协方差矩阵，然后使用具有MCC代价的非线性回归方法来重新构建测量信息。最后，将UT应用于测量方程以获得滤波器状态和协方差矩阵。示例说明了新算法的优越性能。
