非理想關聯(lián)下多傳感器系統(tǒng)誤差的穩(wěn)健估計
doi: 10.11999/JEIT170579
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2.
(海軍工程大學電子工程學院 武漢 430033)
基金項目:
中國博士后科學基金第61批面上項目(2017M613370)
Robust Multisensor Bias Estimation Under Nonideal Association
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2.
(School of Electronic Engineering, Naval Engineering University, Wuhan 430033, China)
Funds:
The 61st Genernal Program Supportting Fund of China Postdoctoral Science Foundation (2017M613370)
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摘要: 在數(shù)據(jù)融合系統(tǒng)中,傳感器自身系統(tǒng)誤差造成其上報融合中心的目標位置狀態(tài)出現(xiàn)系統(tǒng)性偏差,若得不到有效估計與補償,融合系統(tǒng)難以實現(xiàn)預期的性能優(yōu)勢。然而,基于目標關聯(lián)配對關系而構造的超定方程組是系統(tǒng)誤差估計的出發(fā)點。復雜環(huán)境下,受隨機噪聲、系統(tǒng)誤差、虛警、漏報等因素的干擾,數(shù)據(jù)關聯(lián)模塊的輸出結果常常包含錯誤關聯(lián)。針對非理想關聯(lián)下多傳感器系統(tǒng)誤差的穩(wěn)健估計問題,該文提出基于最小截平方的系統(tǒng)誤差穩(wěn)健估計方法,并進一步提出剔除異常方程的重加權最小二乘方法。與最小二乘及最小中值平方相比,所提方法在保證估計器穩(wěn)健性能的前提下,降低了估計結果對隨機噪聲的敏感程度。仿真實驗驗證了所提方法的有效性。
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關鍵詞:
- 多傳感器數(shù)據(jù)融合 /
- 系統(tǒng)誤差估計 /
- 非理想關聯(lián) /
- 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. -
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