| 引用本文: | 孙书利.多模型多传感器信息融合Kalman平滑器[J].控制理论与应用,2005,22(2):211~217.[点击复制] |
| SUN Shu-li.Multi-model and multi-sensor information fusion Kalman smoother[J].Control Theory and Technology,2005,22(2):211~217.[点击复制] |
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| 多模型多传感器信息融合Kalman平滑器 |
| Multi-model and multi-sensor information fusion Kalman smoother |
| 摘要点击 1809 全文点击 1400 投稿时间：2003-03-24 修订日期：2004-04-09 |
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| DOI编号 10.7641/j.issn.1000-8152.2005.2.009 |
| 2005,22(2):211-217 |
| 中文关键词 多模型多传感器系统 标量加权最优信息融合准则 固定滞后平滑器 Kalman滤波方法 |
| 英文关键词 system with multiple models and multiple sensors optimal information fusion criterion weighted by scalars fixed-lag smoother Kalman filtering method |
| 基金项目 国家自然科学基金资助项目(60374026); 黑龙江省教育厅基金资助项目(10541174); 黑龙江大学杰出青年基金资助项目(JC2004-04); 黑龙江大学自动控制重点实验室资助项目. |
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| 中文摘要 |
| 基于标量加权的线性最小方差最优信息融合算法,对多模型多传感器离散线性随机系统,给出了一种分布式标量加权信息融合固定滞后Kalman平滑器.它只需计算加权标量系数,可减小在融合中心的计算负担.当各子系统存在稳态滤波时,又给出了标量加权信息融合稳态平滑器,它计算量小,便于实时应用.并给出了两个子系统之间的平滑误差互协方差阵的计算公式.仿真例子验证了其有效性. |
| 英文摘要 |
| Based on the optimal information fusion algorithm weighted by scalars in the linear minimum variance sense,a distributed information fusion fixed-lag Kalman smoother weighted by scalars is given for discrete linear stochastic system with multiple model and multiple sensors.It only requires the computation of scalar weights,so that the calculated burden in the fusion center can be reduced.The information fusion steady-state smoother weighted by scalars is also given when all subsystems have steady-state filtering.It has a small calculation and is convenient to apply in real time.The computation formula for the smoothing error cross-covariance matrix is given between any two subsystems.A simulation example shows its effectiveness. |