| 引用本文: | 邓自立,李春波.自校正信息融合Kalman平滑器[J].控制理论与应用,2007,24(2):236~242.[点击复制] |
| Deng Zi-li, LI Chun-bo.Self-tuning information fusion Kalmansmoother[J].Control Theory and Technology,2007,24(2):236~242.[点击复制] |
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| 自校正信息融合Kalman平滑器 |
| Self-tuning information fusion Kalmansmoother |
| 摘要点击 1570 全文点击 780 投稿时间：2005-10-13 修订日期：2006-04-24 |
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| DOI编号 10.7641/j.issn.1000-8152.2007.2.013 |
| 2007,24(2):236-242 |
| 中文关键词 多传感器信息融合 加权融合 MA新息模型 系统辨识 噪声方差估计 自校正Kalman平滑器 |
| 英文关键词 multisensor information fusion weighted fusion MA innovation model system identification noise variance estimation ~self-tuning Kalman smoother |
| 基金项目 国家自然科学基金资助项目(60374026);黑龙江大学自动控制重点实验室基金资助项目(F04--01). |
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| 中文摘要 |
| 对含未知噪声统计的多传感器系统,用现代时间序列分析方法,基于滑动平均(MA)新息模型的在线辨识和求解相关函数矩阵方程组,得到了噪声统计的在线估值器,进而在按矩阵加权线性最小方差最优信息融合准则下,提出了自校正信息融合Kalman平滑器.提出了一种按实现收敛性新概念,证明了自校正Kalman融合器按实现收敛于最优Kalman融合器,因而它具有渐近最优性.~同单传感器自校正Kalman平滑器相比,~它可提高平滑精度.一个目标跟踪系统的仿真例子说明了其有效性． |
| 英文摘要 |
| For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on the on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of noise statistics are obtained. Furthermore, under the linear minimum variance optimal information fusion criterion weighted by matrices, a self-tuning information fusion Kalman smoother is presented. A new concept of the convergence in a realization is presented, and it is proved that the self-tuning Kalman fuser converges to the optimal Kalman fuser in a realization, so that it has the asymptotic optimality. Compared with the single-sensor self-tuning Kalman smoother, its accuracy is improved. A simulation example for a target tracking system shows its effectiveness. |
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