| 数据融合中系统误差估计的病态性及改善方法 |
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| 引用本文： | 高青,王钺,杜雄杰,王萌希,山秀明. 数据融合中系统误差估计的病态性及改善方法[J]. 清华大学学报(自然科学版), 2010, 0(4) |
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| 作者姓名： | 高青 王钺 杜雄杰 王萌希 山秀明 |
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| 作者单位： | 清华大学电子工程系; |
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| 摘 要： | 数据融合系统中,传统系统误差估计算法在某些特定物理场景中表现出显著的病态性,估计结果不收敛。为了解决这个问题,该文首先深入分析产生算法病态性的物理场景并将其分为传感器密集型和目标密集型2类,然后将系统误差分解成相对方位系统误差和剩余系统误差2部分分别估计。理论分析和仿真实验表明:该方法在估计相对方位系统误差部分不存在算法病态性;在传感器密集型物理场景中,该方法在估计剩余系统误差部分对病态性进行有效控制;在某些物理场景中,该方法的算法条件数可缩减为传统方法的1%,方位系统误差估计样本方差可缩减为传统方法的1/5。
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| 关 键 词： | 多传感器数据融合 系统误差 病态性 |
Ill-condition and registration of system error estimation in data fusion systems |
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| GAO+Qing,WANG+Yue,DU+Xiongjie,WANG+Mengxi,SHAN+Xiuming. Ill-condition and registration of system error estimation in data fusion systems[J]. Journal of Tsinghua University(Science and Technology), 2010, 0(4) |
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| Authors: | GAO+Qing WANG+Yue DU+Xiongjie WANG+Mengxi SHAN+Xiuming |
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| Affiliation: | GAO+Qing,WANG+Yue,DU+Xiongjie,WANG+Mengxi,SHAN+Xiuming(Department+of+Electronic+Engineering,Tsinghua+University,Beijing+100084,China) |
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| Abstract: | In data fusion systems,traditional system bias estimation algorithms have significant ill-conditioned problems in some special scenes. This paper analyses the scenes which make the traditional algorithms ill-conditioned and classifies the scenes into two parts,dense target type and dense sensor type. The system bias is then decomposed into two parts,relative bear system bias and rest system bias which are estimated independently. Simulations and theoretical analyses indicate that the relative bear system bi... |
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| Keywords: | multi-sensor data fusion system error ill-condition |
| 本文献已被 CNKI 等数据库收录！ |
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