非理想關(guān)聯(lián)下多傳感器系統(tǒng)誤差的穩(wěn)健估計(jì)
doi: 10.11999/JEIT170579 cstr: 32379.14.JEIT170579
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2.
(海軍工程大學(xué)電子工程學(xué)院 武漢 430033)
基金項(xiàng)目:
中國(guó)博士后科學(xué)基金第61批面上項(xiàng)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(bào)融合中心的目標(biāo)位置狀態(tài)出現(xiàn)系統(tǒng)性偏差，若得不到有效估計(jì)與補(bǔ)償，融合系統(tǒng)難以實(shí)現(xiàn)預(yù)期的性能優(yōu)勢(shì)。然而，基于目標(biāo)關(guān)聯(lián)配對(duì)關(guān)系而構(gòu)造的超定方程組是系統(tǒng)誤差估計(jì)的出發(fā)點(diǎn)。復(fù)雜環(huán)境下，受隨機(jī)噪聲、系統(tǒng)誤差、虛警、漏報(bào)等因素的干擾，數(shù)據(jù)關(guān)聯(lián)模塊的輸出結(jié)果常常包含錯(cuò)誤關(guān)聯(lián)。針對(duì)非理想關(guān)聯(lián)下多傳感器系統(tǒng)誤差的穩(wěn)健估計(jì)問題，該文提出基于最小截平方的系統(tǒng)誤差穩(wěn)健估計(jì)方法，并進(jìn)一步提出剔除異常方程的重加權(quán)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(jì)器穩(wěn)健性能的前提下，降低了估計(jì)結(jié)果對(duì)隨機(jī)噪聲的敏感程度。仿真實(shí)驗(yàn)驗(yàn)證了所提方法的有效性。
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關(guān)鍵詞:
- 多傳感器數(shù)據(jù)融合 /
- 系統(tǒng)誤差估計(jì) /
- 非理想關(guān)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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