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非理想关联下多传感器系统误差的稳健估计

田威 黄高明

田威, 黄高明. 非理想关联下多传感器系统误差的稳健估计[J]. 电子与信息学报, 2018, 40(3): 641-647. doi: 10.11999/JEIT170579
引用本文: 田威, 黄高明. 非理想关联下多传感器系统误差的稳健估计[J]. 电子与信息学报, 2018, 40(3): 641-647. doi: 10.11999/JEIT170579
TIAN Wei, HUANG Gaoming. Robust Multisensor Bias Estimation Under Nonideal Association[J]. Journal of Electronics and Information Technology, 2018, 40(3): 641-647. doi: 10.11999/JEIT170579
Citation: TIAN Wei, HUANG Gaoming. Robust Multisensor Bias Estimation Under Nonideal Association[J]. Journal of Electronics and Information Technology, 2018, 40(3): 641-647. doi: 10.11999/JEIT170579

非理想关联下多传感器系统误差的稳健估计

doi: 10.11999/JEIT170579
基金项目: 

中国博士后科学基金第61批面上项目(2017M613370)

Robust Multisensor Bias Estimation Under Nonideal Association

Funds: 

The 61st Genernal Program Supportting Fund of China Postdoctoral Science Foundation (2017M613370)

  • 摘要: 在数据融合系统中,传感器自身系统误差造成其上报融合中心的目标位置状态出现系统性偏差,若得不到有效估计与补偿,融合系统难以实现预期的性能优势。然而,基于目标关联配对关系而构造的超定方程组是系统误差估计的出发点。复杂环境下,受随机噪声、系统误差、虚警、漏报等因素的干扰,数据关联模块的输出结果常常包含错误关联。针对非理想关联下多传感器系统误差的稳健估计问题,该文提出基于最小截平方的系统误差稳健估计方法,并进一步提出剔除异常方程的重加权最小二乘方法。与最小二乘及最小中值平方相比,所提方法在保证估计器稳健性能的前提下,降低了估计结果对随机噪声的敏感程度。仿真实验验证了所提方法的有效性。
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    出版历程
    • 收稿日期:  2017-06-14
    • 修回日期:  2017-11-17
    • 刊出日期:  2018-03-19

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