非理想关联下多传感器系统误差的稳健估计

田威; 黄高明

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

田威¹,
黄高明²

2.
(海军工程大学电子工程学院武汉 430033)

基金项目:

中国博士后科学基金第61批面上项目(2017M613370)

计量
- 文章访问数: 1056
- HTML全文浏览量: 134
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- 被引次数: 0
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

Robust Multisensor Bias Estimation Under Nonideal Association

TIAN Wei¹,
HUANG Gaoming²

2.
(School of Electronic Engineering, Naval Engineering University, Wuhan 430033, China)

Funds:

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

摘要

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。
- 多传感器数据融合 /
- 系统误差估计 /
- 非理想关联 /
- 最小截平方
Abstract: In the data fusion system, sensor biases lead to systematic deviation of the position states of targets reported to the fusion center. If sensor biases could not be estimated and compensated correctly, the fusion system will fail to achieve the expected performance superiority. However, the starting point of sensor bias estimation is the overdetermined equations construted on the biasis of data association. In the complicated environment, with the presence of interference factors such as random errors, sensor biases, false alarms and missed detections, the data association module outputs some misassociations inevitably. In view of the multisensor bias estimation problem under nonideal association, the robust estimation approach based on the least trimmed squares is proposed. Furthermore, the reweighted least squares apporach through eliminating abnormal equations is presented. Compared with the least squares and the least median of squares, the proposed approaches can not only ensure the robust performance on bias estimation, but also are less sensitive to random errors. Simulation results verify the effectiveness of the proposed methods.
- Multisensor data fusion /
- System bias estimation /
- Nonideal association /
- Least Trimmed Squares (LTS)

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参考文献(28)

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