Robust Multisensor Bias Estimation Under Nonideal Association

TIAN Wei; HUANG Gaoming

doi:10.11999/JEIT170579

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 641-647

TIAN Wei, HUANG Gaoming. Robust Multisensor Bias Estimation Under Nonideal Association[J]. Journal of Electronics & 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 & Information Technology, 2018, 40(3): 641-647. doi: 10.11999/JEIT170579

TIAN Wei, HUANG Gaoming. Robust Multisensor Bias Estimation Under Nonideal Association[J]. Journal of Electronics & 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 & Information Technology, 2018, 40(3): 641-647. doi: 10.11999/JEIT170579

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Robust Multisensor Bias Estimation Under Nonideal Association

doi: 10.11999/JEIT170579

TIAN Wei¹,
HUANG Gaoming²

1.
2.
(School of Electronic Engineering, Naval Engineering University, Wuhan 430033, China)

Funds:

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

Received Date: 2017-06-14
Rev Recd Date: 2017-11-17
Publish Date: 2018-03-19

Abstract

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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References(28)

References

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