| 基于REKF的异类传感器异步数据融合算法研究 |
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| 引用本文： | 孙锋利,冯新喜,高全华.基于REKF的异类传感器异步数据融合算法研究[J].电子科技,2004(12):50-53. |
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| 作者姓名： | 孙锋利 冯新喜 高全华 |
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| 作者单位： | 空军工程大学,陕西,西安,710077;长安大学,理学院,陕西,西安,710069 |
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| 摘 要： | 提出了一种基于异类传感器(R和IR)的数据融合目标跟踪算法,两种传感器具有不同的测量维数,量测数据异步采样并以不同的速率传输到融合中心站点.通过时间匹配技术,完成两种异步数据的融合,然后实现滤波器的状态更新.同时文中讨论了一种REKF(旋转推广卡尔曼滤波:Rotation Extended Kalman Filter)算法,可以有效地解决量测非线性和降低计算量的问题.
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| 关 键 词： | 数据融合 目标跟踪 异类传感器 卡尔曼滤波 |
| 修稿时间： | 2004年9月1日 |
A Study of the REKF Algorithm for Asynchronous Data Fusion of Dissimilar Sensors |
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| Sun Fengli,Feng Xinxi,Gao Quanhua.A Study of the REKF Algorithm for Asynchronous Data Fusion of Dissimilar Sensors[J].Electronic Science and Technology,2004(12):50-53. |
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| Authors: | Sun Fengli Feng Xinxi Gao Quanhua |
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| Abstract: | This paper describes an algorithm for fusion of tracks based on radar and IR sensors which have different dimensional measurement data. It's assumed that these sensors are asynchronous and the measurement data are transmitted to a central station at a different rate. By the technique of time matching, two asynchronous data are fused and then the filter is updated according to the fused information. This paper also discusses the rotation extended Kalman filter algorithm for data fusion, which can be used effectively for nonlinear measurement and can reduce the load of calculation. |
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| Keywords: | Data fusion target tracking dissimilar sensor Kalman filter |
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