School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui, 230009, PR China.
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui, 230009, PR China.
ISA Trans. 2023 Jun;137:436-445. doi: 10.1016/j.isatra.2022.12.017. Epub 2023 Jan 2.
To estimate the continuous-discrete nonlinear dynamic systems with non-Gaussian non-zero mean noises, a square-root maximum correntropy cubature Kalman filter with adaptive kernel width and its corresponding smoother are proposed in this paper. Based on the statistical linear regression (SLR) technique, the maximum correntropy criterion (MCC) instead of minimum mean square error (MMSE) is applied to our previously derived continuous-discrete Gaussian estimation method. Compared with MMSE, MCC can not only capture the second-order statistical information of non-Gaussian error, but also utilize its higher-order information. In addition, in order to ensure that MCC can work with an appropriate kernel width, an adaptive kernel width adjustment strategy is given by using the sliding window methodology. Further, for a reliable implementation of the proposed continuous-discrete MCC-based algorithms, they are structurally modified into the square root form. The newly proposed approaches are tested and compared with conventional estimation methods in three commonly used numerical applications. Experimental results show that the proposed algorithms are not only accurate and robust, but also have low computational complexities.
为了估计具有非高斯非零均值噪声的连续离散非线性动态系统，本文提出了一种具有自适应核宽的平方根最大相关摘容积卡尔曼滤波器及其相应的平滑器。基于统计线性回归（SLR）技术，本文将最大相关摘准则（MCC）而不是最小均方误差（MMSE）应用于我们之前推导出的连续离散高斯估计方法。与 MMSE 相比，MCC 不仅可以捕获非高斯误差的二阶统计信息，还可以利用其高阶信息。此外，为了确保 MCC 能够使用适当的核宽，本文通过使用滑动窗口方法给出了一种自适应核宽调整策略。进一步地，为了可靠地实现所提出的基于连续离散 MCC 的算法，将它们结构上修改为平方根形式。在三个常用的数值应用中，对新提出的方法进行了测试，并与传统估计方法进行了比较。实验结果表明，所提出的算法不仅准确和鲁棒，而且具有较低的计算复杂度。
