| 引用本文: | 林孝工,焦玉召,梁坤,李恒.相关噪声下非线性滤波及在动力定位中的应用[J].控制理论与应用,2016,33(8):1081~1088.[点击复制] |
| LIN Xiao-gong,JIAO Yu-zhao,LIANG Kun,LI Heng.Application of the nonlinear filtering algorithm with a correlation noise in the dynamic positioning[J].Control Theory and Technology,2016,33(8):1081~1088.[点击复制] |
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| 相关噪声下非线性滤波及在动力定位中的应用 |
| Application of the nonlinear filtering algorithm with a correlation noise in the dynamic positioning |
| 摘要点击 2056 全文点击 1811 投稿时间：2015-10-28 修订日期：2016-06-04 |
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| DOI编号 10.7641/CTA.2016.50853 |
| 2016,33(8):1081-1088 |
| 中文关键词 相关噪声 贝叶斯估计 容积卡尔曼滤波 动力定位 |
| 英文关键词 correlation noise Bayesian estimation cubature Kalman filtering dynamic positioning |
| 基金项目 国家自然科学基金项目(51309062), 重大专项“深水铺管起重船及配套工程技术”(2011ZX05027–002)资助. |
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| 中文摘要 |
| 针对实际系统状态估计具有互相关噪声的情况, 研究了互相关噪声下非线性系统状态估计问题. 首先基于
贝叶斯理论推导出新的互相关噪声下的贝叶斯估计算法. 然后使用三阶球面径向基(spherical-radial)规则计算贝叶
斯估计中的非线性积分, 当噪声互相关时, 基于扩展卡尔曼滤波的思想分别计算状态矩阵和观测矩阵的Jacobi矩阵,
可得互相关噪声下的容积卡尔曼滤波(cubature Kalman filtering with one-step auto-correlated and two-step crosscorrelated
noise, CKF–CCN); 当噪声不相关时, 可得容积卡尔曼滤波(cubature Kalman filtering, CKF)及其平方根形
式(SCKF). 最后通过动力定位系统仿真实验, 表明提出的CKF–CCN的估计精度要高于SCKF和仅考虑一步互相关
的平方根容积卡尔曼滤波(SCKF–CN). |
| 英文摘要 |
| In view of the situation that the state estimates have correlated noise in practice, the state estimation of
nonlinear system under correlation noise is studied. Firstly, the new Bayesian estimation with correlated noise is obtained
based on the Bayesian theory. Secondly, the third-degree-spherical-radial rule is used to solve the nonlinear integral, if the
noise is correlated then the Jacobi matrix of the state matrix and the observation matrix are computed respectively and the
cubature Kalman filtering with one-step auto-correlated and two-step cross-correlated noise (CKF–CCN) is obtained; if the
noise is uncorrelated then the cubature Kalman filtering (CKF) algorithm and its square root form (SCKF) are obtained.
Finally, through the simulation experiment of dynamic positioning and the results illustrate that the estimation accuracy of
proposed CKF–CCN algorithm is higher than the SCKF algorithm and the squared root cubature Kalman filtering algorithm
which only considering one-step cross-correlated noise (SCKF–CN). |
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